{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.3.0\n",
      "ba41b1434002b1fce6e596386861bca2a890fb6f\n"
     ]
    }
   ],
   "source": [
    "############################################################\n",
    "# Plots of meta-stable Maxwell loops\n",
    "# Inspired by https://doi.org/10.1134/S0036024406040030\n",
    "# Math tricks taken from: http://math.stackexchange.com/q/416823/92706\n",
    "# Plot also shown on page 79 of https://doi.org/10.15480/882.1207\n",
    "############################################################\n",
    "\n",
    "# load some bits and pieces\n",
    "import numpy as np\n",
    "from numpy.linalg import solve\n",
    "from numpy.linalg import lstsq\n",
    "from numpy import log\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import CoolProp as CP\n",
    "from CoolProp.CoolProp import PropsSI\n",
    "\n",
    "# Check: CoolProp version\n",
    "print(CP.__version__)\n",
    "print(CP.__gitrevision__)\n",
    "\n",
    "# Constants\n",
    "eps = 1e-3\n",
    "kilo = 1e3\n",
    "Mega = 1e6\n",
    "golden = (1 + 5 ** 0.5) / 2\n",
    "width = 12.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R = 8.31451\n",
      "MM = 0.0440098\n",
      "Rs = 188.92405782348476\n",
      "T_crt = 304.1282\n",
      "T_trp = 216.592\n"
     ]
    }
   ],
   "source": [
    "# Set FluidName\n",
    "FluidName = 'CO2'\n",
    "nPoints = 1000\n",
    "# pick any int smaller than nPoints\n",
    "myIdx = 860 \n",
    "\n",
    "# Constants, triple and critical data\n",
    "R = PropsSI('GAS_CONSTANT',FluidName)\n",
    "MM = PropsSI('MOLAR_MASS',FluidName)\n",
    "Rs = R/MM\n",
    "T_crt = PropsSI('T_CRITICAL',FluidName)\n",
    "T_trp = PropsSI('T_TRIPLE',FluidName)\n",
    "p_crt = PropsSI('P_CRITICAL',FluidName)\n",
    "p_trp = PropsSI('P_TRIPLE',FluidName)\n",
    "p_max = PropsSI('P_MAX',FluidName)\n",
    "d_crt = PropsSI('RHOMASS_CRITICAL',FluidName)\n",
    "v_crt = 1/d_crt\n",
    "d_trp_liq = PropsSI('D','T',T_trp,'Q',0,FluidName)\n",
    "d_trp_vap = PropsSI('D','T',T_trp,'Q',1,FluidName)\n",
    "print(\"R = \" + str(R))\n",
    "print(\"MM = \" + str(MM))\n",
    "print(\"Rs = \" + str(Rs))\n",
    "print(\"T_crt = \" + str(T_crt))\n",
    "print(\"T_trp = \" + str(T_trp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculation of the coefficients for the metastable region interpolation happens in this cell\n",
    "\n",
    "T_sat = np.linspace(T_trp, T_crt-eps, num=nPoints)\n",
    "# empty arrays\n",
    "# vap side\n",
    "delta_vap = np.empty(nPoints)\n",
    "tau_vap = np.empty(nPoints)\n",
    "p_vap = np.empty(nPoints)\n",
    "d_vap = np.empty(nPoints)\n",
    "v_vap = np.empty(nPoints)\n",
    "f_vap = np.empty(nPoints)\n",
    "dP_dD_T_vap = np.empty(nPoints)\n",
    "d2P_dD2_T_vap = np.empty(nPoints)\n",
    "d2P_dDdT_vap = np.empty(nPoints)\n",
    "# liq side\n",
    "delta_liq = np.empty(nPoints)\n",
    "tau_liq = np.empty(nPoints)\n",
    "p_liq = np.empty(nPoints)\n",
    "d_liq = np.empty(nPoints)\n",
    "v_liq = np.empty(nPoints)\n",
    "f_liq = np.empty(nPoints)\n",
    "dP_dD_T_liq = np.empty(nPoints)\n",
    "d2P_dD2_T_liq = np.empty(nPoints)\n",
    "d2P_dDdT_liq = np.empty(nPoints)\n",
    "# metastable coeffs: \n",
    "AShape = (8,8)\n",
    "A = np.empty(AShape)\n",
    "b = np.empty(8)\n",
    "xShape = (nPoints,8)\n",
    "x = np.empty(xShape)\n",
    "\n",
    "HEOS = CP.AbstractState(\"HEOS\", FluidName)\n",
    "# get values from CoolProp\n",
    "for idx in range(0,nPoints):\n",
    "    # AT the vap line\n",
    "    HEOS.update(CP.QT_INPUTS, 1, T_sat[idx])  \n",
    "    delta_vap[idx] = HEOS.delta() \n",
    "    tau_vap[idx] = HEOS.tau()\n",
    "    p_vap[idx] = HEOS.p()\n",
    "    d_vap[idx] = HEOS.rhomass()\n",
    "    f_vap[idx] = Rs*T_sat[idx]*( HEOS.alpha0() + HEOS.alphar() )    \n",
    "    #f_vap[idx] = HEOS.umass() - T_sat[idx]*HEOS.smass()\n",
    "    dP_dD_T_vap[idx] = HEOS.first_partial_deriv(CP.iP, CP.iDmass, CP.iT)\n",
    "    d2P_dD2_T_vap[idx] = HEOS.second_partial_deriv(CP.iP, CP.iDmass, CP.iT, CP.iDmass, CP.iT)\n",
    "    d2P_dDdT_vap[idx] = HEOS.second_partial_deriv(CP.iP, CP.iDmass, CP.iT, CP.iT, CP.iDmass)     \n",
    "    \n",
    "    # AT the liq line\n",
    "    HEOS.update(CP.QT_INPUTS, 0, T_sat[idx])  \n",
    "    delta_liq[idx] = HEOS.delta() \n",
    "    tau_liq[idx] = HEOS.tau()\n",
    "    p_liq[idx] = HEOS.p() \n",
    "    d_liq[idx] = HEOS.rhomass() \n",
    "    f_liq[idx] = Rs*T_sat[idx]*( HEOS.alpha0() + HEOS.alphar() )\n",
    "    # f_liq[idx] = HEOS.umass() - T_sat[idx]*HEOS.smass()\n",
    "    dP_dD_T_liq[idx] = HEOS.first_partial_deriv(CP.iP, CP.iDmass, CP.iT)\n",
    "    d2P_dD2_T_liq[idx] = HEOS.second_partial_deriv(CP.iP, CP.iDmass, CP.iT, CP.iDmass, CP.iT)   \n",
    "    d2P_dDdT_liq[idx] = HEOS.second_partial_deriv(CP.iP, CP.iDmass, CP.iT, CP.iT, CP.iDmass)   \n",
    "\n",
    "    # calculate metastable coeffs by solving Ax=b\n",
    "    A = np.array([  [1/tau_vap[idx], -1/delta_vap[idx]/tau_vap[idx],  log(delta_vap[idx]),          delta_vap[idx],     delta_vap[idx]**2/2,       delta_vap[idx]**3/3,         delta_vap[idx]**4/4,        delta_vap[idx]**5/5 ], \n",
    "                    [1/tau_liq[idx], -1/delta_liq[idx]/tau_liq[idx],  log(delta_liq[idx]),          delta_liq[idx],     delta_liq[idx]**2/2,       delta_liq[idx]**3/3,         delta_liq[idx]**4/4,        delta_liq[idx]**5/5 ], \n",
    "                    [             0,             d_crt/tau_vap[idx], d_crt*delta_vap[idx], d_crt*delta_vap[idx]**2, d_crt*delta_vap[idx]**3,    d_crt*delta_vap[idx]**4,    d_crt*delta_vap[idx]**5,    d_crt*delta_vap[idx]**6 ], \n",
    "                    [             0,             d_crt/tau_liq[idx], d_crt*delta_liq[idx], d_crt*delta_liq[idx]**2, d_crt*delta_liq[idx]**3,    d_crt*delta_liq[idx]**4,    d_crt*delta_liq[idx]**5,    d_crt*delta_liq[idx]**6 ], \n",
    "                    [             0,                              0,                    1,        2*delta_vap[idx],     3*delta_vap[idx]**2,        4*delta_vap[idx]**3,        5*delta_vap[idx]**4,        6*delta_vap[idx]**5 ], \n",
    "                    [             0,                              0,                    1,        2*delta_liq[idx],     3*delta_liq[idx]**2,        4*delta_liq[idx]**3,        5*delta_liq[idx]**4,        6*delta_liq[idx]**5 ], \n",
    "                    [             0,                              0,                    0,                 2/d_crt,  6*delta_vap[idx]/d_crt, 12*delta_vap[idx]**2/d_crt, 20*delta_vap[idx]**3/d_crt, 30*delta_vap[idx]**4/d_crt ], \n",
    "                    [             0,                              0,                    0,                 2/d_crt,  6*delta_liq[idx]/d_crt, 12*delta_liq[idx]**2/d_crt, 20*delta_liq[idx]**3/d_crt, 30*delta_liq[idx]**4/d_crt ]])\n",
    "    A = Rs*T_crt*A\n",
    "    b = np.array([f_vap[idx], f_liq[idx], p_vap[idx], p_liq[idx], dP_dD_T_vap[idx], dP_dD_T_liq[idx], d2P_dD2_T_vap[idx], d2P_dD2_T_liq[idx]])\n",
    "    x[idx] = solve(A,b)\n",
    "    \n",
    "    # for validation\n",
    "    if (abs(idx-myIdx)<0.9):\n",
    "        print(np.allclose(np.dot(A, x[idx]), b))\n",
    "        print(A)\n",
    "        print(b)\n",
    "        print(x[idx])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just some validation plots\n",
    "plt.figure(figsize=(width,width*4/2/golden))\n",
    "\n",
    "plt.subplot(4,2,1)\n",
    "plt.plot(T_sat, f_vap, color='red')\n",
    "plt.plot(T_sat, f_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.ylabel('Helmholtz energy')\n",
    "\n",
    "plt.subplot(4,2,2)\n",
    "plt.plot(d_vap, T_sat, color='red')\n",
    "plt.plot(d_liq, T_sat, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('Density in kg/m³')\n",
    "plt.ylabel('Temperature in K')\n",
    "\n",
    "plt.subplot(4,2,3)\n",
    "plt.plot(T_sat, p_vap, color='red')\n",
    "plt.plot(T_sat, p_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.ylabel('Pressure in Pa')\n",
    "\n",
    "plt.subplot(4,2,4)\n",
    "plt.plot(d_vap, p_vap, color='red')\n",
    "plt.plot(d_liq, p_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('Density in kg/m³')\n",
    "plt.ylabel('Pressure in Pa')\n",
    "\n",
    "plt.subplot(4,2,5)\n",
    "plt.plot(T_sat, dP_dD_T_vap, color='red')\n",
    "plt.plot(T_sat, dP_dD_T_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.yscale('log')\n",
    "plt.ylabel('dP_dD_T')\n",
    "\n",
    "plt.subplot(4,2,6)\n",
    "plt.plot(d_vap, dP_dD_T_vap, color='red')\n",
    "plt.plot(d_liq, dP_dD_T_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Density in kg/m³')\n",
    "plt.ylabel('dP_dD_T')\n",
    "\n",
    "plt.subplot(4,2,7)\n",
    "plt.plot(T_sat, d2P_dD2_T_vap, color='red')\n",
    "plt.plot(T_sat, d2P_dD2_T_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.ylabel('d2P_dD2_T')\n",
    "plt.xlabel('Temperature in K')\n",
    "\n",
    "plt.subplot(4,2,8)\n",
    "plt.plot(d_vap, d2P_dD2_T_vap, color='red')\n",
    "plt.plot(d_liq, d2P_dD2_T_liq, color='blue')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.xlabel('Density in kg/m³')\n",
    "plt.ylabel('d2P_dD2_T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tau_iso = 1.0417217720566936\n",
      "T_iso = 291.94762762762764\n",
      "p_sat(T_iso) = 5569191.928520719\n",
      "d_vap(T_iso) = 185.17587066325405\n",
      "d_liq(T_iso) = 785.8632178765519\n",
      "coeffs = [-0.38011836 -0.0331094   1.21278872 -2.00233849  1.1397745   0.285719\n",
      " -0.5838537   0.17728102]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$v/v_c$')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hw8nIyLjka7799ttZsGABAGvXrqVLly74+PgUfH7o0KF07NiRtm3b8sEHHwDw008/ERISQmpqKk6nkx49erBy5UpeeeUVpk2bBsBf/vIX+vbtC8Dq1asZPXr0VY3rSl5//XXatWtHu3btmDp16p9+/ELXK/2ZFYfPlR+irkYQ1TjsOO2xa+FXqlTJ/QdVttCWcmhLObSlHO5u+fjjsH27Ww9JeDj8bi54SYcOHWLhwoV88MEHdOrUiY8//pgNGzawbNky/vOf//D5558zadIk+vbty6xZs0hLSyMqKoprr72WF154gejoaN5++20A0tPTWbduHT4+PqxatYpnnnmGxYsXM336dB577DFGjRrF+fPnyc/PZ/LkyezevZvtv77ovLw8lixZQpUqVUhNTaVLly4MHjwYgP379zNz5ky6d+/OuHHjePfdd5kwYULBa0hNTeXFF19k1apVVKxYkZdffpnXX3+d55577qLXGxISwtKlSzl9+jTz589n5MiRrFy5suDzs2bNokaNGmRlZdGpUyduueUWmjRpwpNPPskDDzxA586dadOmDQMGDKBKlSq89tprPProo0RHR5OTk0Nubi4bNmygR48eVzWuPxMTE8Ps2bPZvHkzpmnSuXNnevXqhdPpvOTHq1evfsU/s+LSCb6bBfnX4TvHz3RsaHL0qOH24+fm5rr9mMoe2lIObSmHtpRDSsvg4GBCQ0MB68Thfv36YRgGoaGhJCQkANYq9LJly5gyZQoA2dnZJCYmXnSsM2fOMGbMGA4ePIhhGAV/Rl27dmXSpEkkJSUxbNgwQkJCLnquaZo888wzrFu3DofDwbFjx0j59XKBjRo1onv37gCMHj2aadOmFZqs/vjjj+zdu7fgMefPn6dr166Xfc3Dhg3jk08+YfPmzbz11luFPjdt2jSWLFkCwNGjRzl48CCBgYGMHz+ehQsXMn369II3JR07diQmJoazZ8/i5+dHREQE0dHRrF+/nmnTpl31uC5nw4YN3HzzzQWX9Bw2bBjr16/HNM1Lfnzw4MFX/DMrLp3gu1lQ1SAycnZQO/AsW2OruP34Tk9cXF/ZQlvKoS3l0JZyuLvllVbaPcXPz6/gfzscjoLfOxwO8vLyAGvyvXjxYlq2bFnouZs3by70+4kTJ9KnTx+WLFlCQkICvXv3BuCOO+6gc+fOfPnllwwcOJAZM2bQtGnTQs+dN28ev/zyCzExMfj6+hIUFER2djYAhlF4QfOPvzdNk/79+zN//vwiveaRI0cSERHBmDFjcDh+202+du1aVq1axaZNmwgICKB3794FY8jMzCTp160TGRkZVK5cuWCcs2fPplu3boSFhbFmzRri4+Np3bo18fHxVzWuyzFN86o+Dlf+Mysu3YPvZheuhV+xfKJHbnYVEBDg3gMq22hLObSlHNpSjrLUcuDAgbz11lsFE8pt27YBULly5UJXpDlz5kzBCatz5swp+Pjhw4dp2rQpjz76KIMHD2bnzp2XfG7t2rXx9fVlzZo1/PTTTwWfS0xMZNOmTQDMnz+fa665ptD4unTpwg8//FBwZaPMzEwOHDhw2dfTuHFjJk2axEMPPVRogn/mzBmqV69OQEAAcXFx/PjjjwWfe/LJJxk1ahQvvPAC9957b8HHe/bsyZQpU+jZsyc9evRg+vTphIeHYxjGVY/rcnr27Mnnn39OZmYm586dY8mSJfTo0eOyHy/Kn1lx6QTfzS5cC79cuYPk5sKJE+49/qlTp9x7QGUbbSmHtpRDW8pRllpOnDiR3NxcwsLCaNeuHRMnTgSgT58+7N27t+Ak27///e88/fTTdO/enfz8/ILnL1iwgHbt2hEeHk5cXBx33XUXgYGBdO/enXbt2vHEE08watQooqOjiYyMZN68ebRq1arg+a1bt+bDDz8kLCyMU6dO8eCDDxYaX61atZgzZw633347YWFhdOnShbi4uD99Tffffz/NmjUr+CkFwHXXXUdeXh5hYWFMnDiRLl2sy5N///33bN26tWCSX758eWbPng1Ajx49SE5OpmvXrtSpUwd/f/+CSbYr47qUiIgIxo4dS1RUFJ07d2b8+PF06NDhsh8vyp9ZcRl/9uMDbxUZGWlezXVX3el08hFqfNCUu+P/xeyPnmPzZoiKct/xs7Ky9KYPQmhLObSlHNpSDne03LdvH61bt3bTiJQrnE5noVV8CRISErjxxhvZvXv3nz7uUn//DMOIMU0z8kpfQ9afWClw4Vr4Wb7WLbKPHHHv8Y+4+4DKNtpSDm0ph7aUQ1vKkJOTY/cQvJJO8N3McDhonlWBX/ytnyD8eoK72/z+R2LKu2lLObSlHNpSDm0pg7+/v91DcLugoKArrt4Xl07wPSDEqEmC/3Fq1nT/Cv52d1+EV9lGW8qhLeXQlnK4q6XErczexN03gPIWxf17pxN8D2heqTEJlfJo0jjf7RP8iIgI9x5Q2UZbyqEt5dCWcrijpb+/PydPntRJvo0uXEO+LDFNk5MnTxbrpxd6HXwPCKnTmvzUH6hd4xfiE+q69dgxMTF07NjRrcdU9tCWcmhLObSlHO5o2bBhQ5KSkvjll1/cNCp1tc6fP0/58uXtHkaJ8/f3p2HDhi4/Xyf4HtA8KAJSoWKFeBIS6uJ0grtOANf/45FDW8qhLeXQlnK4o6Wvry/BwcFuGI1SJUu36HhASNueABjl9nD+PCQnu+/YsbGx7juYspW2lENbyqEt5dCWMmhH1+gE3wNqNW5N5RzI8bXuJOfOK+mEh4e772DKVtpSDm0ph7aUQ1vKoB1doxN8DzAcDkKyKnCqgnUGvztPtHXlDmuqdNKWcmhLObSlHNpSBu3oGp3ge0hzoybHq1nXOHXnCr7uBZRDW8qhLeXQlnJoSxm0o2t0gu8hIZUa81O1DOrVdXL4sPuOe/z4cfcdTNlKW8qhLeXQlnJoSxm0o2t0gu8hzWu3It8BDeqd4dAh9x23Ro0a7juYspW2lENbyqEt5dCWMmhH1+gE30NaNe8CQLXKhzl40H3HLat3dJNIW8qhLeXQlnJoSxm0o2t0gu8hrSMGAODjv4Off4b0dPcc1+GuC+or22lLObSlHNpSDm0pg3Z0jf6peUjV2o1pkOEgy38rgNtW8X19fd1zIGU7bSmHtpRDW8qhLWXQjq7RCb4HtcmtRmqVaMB9E/yMjAz3HEjZTlvKoS3l0JZyaEsZtKNrbJngG4Yx3DCMPYZhOA3DiPyTx1UzDGORYRhxhmHsMwyja0mOs7jaVGjMkXrWpTIPHHDPMWvWrOmeAynbaUs5tKUc2lIObSmDdnSNXSv4u4FhwLorPO5NYIVpmq2A9sA+Tw/MnVrXbkNmxWwa1Mtx2wQ/KSnJPQdSttOWcmhLObSlHNpSBu3oGlsm+KZp7jNNc/+fPcYwjCpAT2Dmr885b5pmWkmMz13aNLd+4FAr8Ljbtug0b97cPQdSttOWcmhLObSlHNpSBu3omtK8B78p8Asw2zCMbYZhzDAMo+LlHmwYxn2GYUQbhhGdnJxMamoqycnJHDt2jNOnTxMfH09WVhZ79+7F6XQSGxsLQExMDACxsbE4nU727t1LVlYW8fHxnD59mmPHjnHheAkJCWRkZBAXF0deXh47duwodIwL/921axc5OTlUqN4SgPJ+u9i/30lKygkSExNJT0/n4MGD5OTksGvXrkseY8eOHeTl5REXF0dGRgYJCQmkpqaydetWW1/TwYMHSU9PJzExkRMnTnDiRPFfk92d7HpNa9euFfeaJHYqymvaunWruNcksVNRXtOePXvEvSaJnYrymqKjo8W9JomdrvSafv89KeU1FadTURmmaRb5wVfDMIxVQN1LfOpZ0zSX/vqYtcAE0zSjL/H8SOBHoLtpmpsNw3gTSDdNc+KVvnZkZKQZHX3RIW1R50kHwVteZPPaZzhxAmrVsntESimllFLKGxmGEWOa5mXPX73AYyv4pmlea5pmu0v8WlrEQyQBSaZpbv7194uACM+M1nPa5FQlrar1Li0urvjHu/COT3k/bSmHtpRDW8qhLWXQjq4ptVt0TNP8GThqGEbLXz/UDyj6zyZKiVD/xhxtaF0Lf/fu4h+vY8eOxT+IKhW0pRzaUg5tKYe2lEE7usauy2TebBhGEtAV+NIwjG9+/Xh9wzC++t1D/w+YZxjGTiAc+E/Jj7Z4wutHkFnzKBUr5rFnT/GPp+9k5dCWcmhLObSlHNpSBu3oGo/twbdTadqDH/vdPDquH03I3CM0aBzEmjV2j0gppZRSSnkj2/fgK0ubqBvwyYeAKrvcskXnwlnYyvtpSzm0pRzaUg5tKYN2dI1O8D3Mv1I1WmX4kVtlC6mpcOJE8Y7XokUL9wxM2U5byqEt5dCWcmhLGbSja3SCXwLCjfqk1N4CFP9E28TERDeMSJUG2lIObSmHtpRDW8qgHV2jE/wSEF6zLSeb7AQo9om2derUccOIVGmgLeXQlnJoSzm0pQza0TU6wS8B4S16QqWfqVwpq9gT/LS0NPcMStlOW8qhLeXQlnJoSxm0o2t0gl8CwjsPBQNqBsazc2fxjuXv7++eQSnbaUs5tKUc2lIObSmDdnSNTvBLQGDDEJqe9aFcjR/ZsQPy8+0ekVJKKaWUkkon+CUkyqzPybobyMyE/ftdP052drb7BqVspS3l0JZyaEs5tKUM2tE1OsEvIZ1rR3A6yLr5Vmys68epVq2am0ak7KYt5dCWcmhLObSlDNrRNTrBLyGdwwZBzTjK++YWa4KfkpLivkEpW2lLObSlHNpSDm0pg3Z0jU7wS0iHHrfhSz6BtfYXa4LfuHFj9w1K2UpbyqEt5dCWcmhLGbSja3SCX0L8K1Wj/dkAjMDNbNsGTqdrxzlw4IB7B6Zsoy3l0JZyaEs5tKUM2tE1OsEvQZ39mnGy0UbS0yE+3rVjhIaGundQyjbaUg5tKYe2lENbyqAdXaMT/BLUpUl3chrFALB1q2vHiImJceOIlJ20pRzaUg5tKYe2lEE7usYwTdPuMbhdZGSkGR0dbfcwLpK4dxNNPumB38tnGX9vBd5+2+4RKaWUUkopb2EYRoxpmpFXepyu4Jegxm26EpRpUKVONBs3unYMfScrh7aUQ1vKoS3l0JYyaEfX6Ap+CRszoRkLN43l/OZ/kJZmUKmS3SNSSimllFLeQFfwS6lejXuSFbyR/HzDpX34O3bscP+glC20pRzaUg5tKYe2lEE7ukYn+CWsV887oeGPAC5t02nbtq2bR6Tsoi3l0JZyaEs5tKUM2tE1OsEvYU3DetMgP50qNQ64NME/dOiQ+welbKEt5dCWcmhLObSlDNrRNTrBL2GGw0E/M4icRmvZsMEkL+/qnt+wYUPPDEyVOG0ph7aUQ1vKoS1l0I6u0Qm+DQY2v46ckNWkpxvExl7dc1NTUz0zKFXitKUc2lIObSmHtpRBO7pGJ/g26H/9IxC0BoDVq6/uuZX0sjtiaEs5tKUc2lIObSmDdnSNTvBtUKtxazrlZRIQuJvvvru65+bm5npmUKrEaUs5tKUc2lIObSmDdnSNTvBtcl2VCDKbf8uGDU6ys4v+PKfT6blBqRKlLeXQlnJoSzm0pQza0TU6wbfJdZ1HQdPvyM52sGlT0Z8XEBDguUGpEqUt5dCWcmhLObSlDNrRNTrBt0nUtWOoUed7HI7zfP110Z936tQpzw1KlShtKYe2lENbyqEtZdCOrtEJvk18yvsz1KcuRuPv+WJZfpGfV79+fQ+OSpUkbSmHtpRDW8qhLWXQjq7RCb6NhoXfQX6rL4jbX474+KI958iRI54dlCox2lIObSmHtpRDW8qgHV2jE3wbXTv0r1QMWg7A8uVFe06rVq08OCJVkrSlHNpSDm0ph7aUQTu6Rif4NvKrWIXBAU7KBe5h2dKibdPZvn27h0elSoq2lENbyqEt5dCWMmhH1ximado9BreLjIw0o6Oj7R5GkSyZ8yTDZlaj3Ka/k5JSjsBAu0eklFJKKaVKI8MwYkzTjLzS43QF32bXD3+WyiGfkp9fjs8+u/LjY2JiPD8oVSK0pRzaUg5tKYe2lEE7ukZX8EuBB//ejvdnLqZH2yZ8v87f7uEopZRSSqlSSFfwvciYvo9jhn7C+g3lSU7+88fNox2TAAAgAElEQVTGxsaWzKCUx2lLObSlHNpSDm0pg3Z0ja7glwKm00nQuA4kfriDqVPhsccu/1in04nDoe/LJNCWcmhLObSlHNpSBu1YmK7gexHD4eDhkAZQL4Z33zrDn73niouLK7mBKY/SlnJoSzm0pRzaUgbt6Bqd4JcS4+56A5/wDzgQX5WtWy//uODg4JIblPIobSmHtpRDW8qhLWXQjq7RCX4pUbNRS4Y32wy+53j7zYzLPu748eMlOCrlSdpSDm0ph7aUQ1vKoB1doxP8UuSvQ/4K7eazYFE50tMv/ZgaNWqU7KCUx2hLObSlHNpSDm0pg3Z0jU7wS5HIa+8irOVHnD9fgenv5VzyMZmZmSU8KuUp2lIObSmHtpRDW8qgHV2jE/xSZvLgftBkLS9PziA39+LP65nkcmhLObSlHNpSDm0pg3Z0jf6plTLXjfgHzcLf5FRaIB/PO3/R5319fW0YlfIEbSmHtpRDW8qhLWXQjq7RCX4pYzgcvDw4BGrt5h/P/ILTWfjzGRmXPwFXeRdtKYe2lENbyqEtZdCOrtEJfil085j/ENzxNZKSG/DRnMJ/sWvWrGnTqJS7aUs5tKUc2lIObSmDdnSNTvBLIUc5H2bc2x7q7OCvT6QX2ouflJRk38CUW2lLObSlHNpSDm0pg3Z0jU7wS6m+wx6nU+dXOXWqPq9P/u0vd/PmzW0clXInbSmHtpRDW8qhLWXQjq7RCX4p9tE/7sRovJbn/lOR1FTrY3v27LF3UMpttKUc2lIObSmHtpRBO7rGME3T7jG4XWRkpBkdHW33MNzi/x4Zx9vvvc+Anrv4Zk2E3cNRSimllFI2MQwjxjTNyCs9TlfwS7nXXp1GYIdprFwbwfLPkomJibF7SMpNtKUc2lIObSmHtpRBO7pGV/C9wLpl8+h1dxQBuRU49lN9qlXX92VKKaWUUmWNruAL0nPwKB4a/i6ZGXXp03Ot3cNRbqKrEnJoSzm0pRzaUgbt6BpdwfcSzvw8QrpP4fDmp/j7/63j5Wk97R6SUkoppZQqQbqCL4yjnA+bFtxChaAVvPJOVxZ9tNvuIali2rVrl91DUG6iLeXQlnJoSxm0o2t0gu9FajcJ4Zt383BUO8zI+2vz49p4u4ekiqFFixZ2D0G5ibaUQ1vKoS1l0I6u0Qm+l6nbvCVzXthEvgE9B5dnV4ze4c1bJSYm2j0E5SbaUg5tKYe2lEE7ukYn+F6mTp063PnwWN6euJTc85XpdG022zYdtHtYygV16tSxewjKTbSlHNpSDm0pg3Z0jU7wvUxaWhoADz91L68+9Sk5WTXp1D+ArxfLOqm4LLjQUnk/bSmHtpRDW8qgHV2jE3wv4+/vX/C/Jzx/Hx++9g1OA24Y1ZzX/vW5jSNTV+v3LZV305ZyaEs5tKUM2tE1OsH3cnc9PIKVH8XhWymJCc8PZmD//5Gf57R7WEoppZRSyiY6wfcy2dnZF33s2qH9SIiuTqNWS1i5ajS1m25g46ptNoxOXY1LtVTeSVvKoS3l0JYyaEfX6ATfy1SrVu2SH68X1ICE3Tdz+y3vcSqlA91vaM4dt84hLzevhEeoiupyLZX30ZZyaEs5tKUM2tE1OsH3MikpKZf9nKOcg48XPci6z3dTvW408xePpUqD3Ux9cVEJjlAV1Z+1VN5FW8qhLeXQljJoR9foBN/LNG7c+IqP6TGoK6mHe/HQ3TM5n12dv0y8lXrNvmHBzJUlMEJVVEVpqbyDtpRDW8qhLWXQjq7RCb6XOXDgQJEe5yjn4J1Z95ByoCL9r53Jz0ldGTl+AHWafssHr+vVdkqDorZUpZ+2lENbyqEtZdCOrjFM07R7DG4XGRlpRkfrdeF/78i+nxj/8Hes2TQYMzuQyvV+ZFS/3Ux+eQhV69eye3hKKaWUUuoKDMOIMU0z8kqP0xV8LxMTE+PS84JbN2H1d3eTfMDJ0BtnkZ0dyPT/jad6c+jSaSYrZiwDgW/2SjNXW6rSR1vKoS3l0JYyaEfX6Ap+GZWXl8+UycuYNjeA5EPXglmOCnW20rvjZp5+ogM9ene3e4hKKaWUUup3dAVfKHe9k/XxKcdT/7iZ4wcGEh2byJChH+Nw+vH1V4/Qs29nKjddydA732H9zl1u+XrqYroqIYe2lENbyqEtZdCOrtEVfFXIms9ieen1A6zb0YWcjCDASYWGW+na4xiPPtiOm7qH4HAYdg9TKaWUUqrM0RV8oXbs2OHR4/cZFsHKDSPJOt2IlW98zfUdP6Bclg/fzR/G0J4t8K91hC7DvmfGZ4fIzZX35rAkebqlKjnaUg5tKYe2lEE7ukZX8L1MXl4ePj4+JftFU1OJnfYxr36ezMqTPTmV0hfy/XBUSKN1tyPcObwa948Iolo1Xdm/Gra0VB6hLeXQlnJoSxm0Y2GlegXfMIzhhmHsMQzDaRjGZQdpGMZffn3cbsMw5huG4V+S4yyNDh06VPJftGZNIl54lPk7/sPJZbU5PO4v3N/qdurU+5w9Gxvx1APBVK+ZS9PIgzw96TgJCSU/RG9kS0vlEdpSDm0ph7aUQTu6xpYVfMMwWgNO4H1ggmmaFy23G4bRANgAtDFNM8swjE+Br0zTnHOl40tewc/IyKBSpUp2DwOysmDJElI+/IA3EnJZ4DuYhJTBkNoagDrNUrh1qC9jRtSgY0dw6Gawi5SalqrYtKUc2lIObSmDdiysVK/gm6a5zzTN/UV4qA9QwTAMHyAAOO7ZkZV+qampdg/BUqEC3HEHdb5Zy+Rv5nHk1ixSavTnudAWBHd+gpS8ON55vSpRURBYJ4t77s1n+XLrfYGylJqWqti0pRzaUg5tKYN2dE2pXVc1TfMYMAVIBJKBM6ZprrR3VPYrle9ig4Lg+eepvS+Rf73+LoeDk0g8N4Cnu9Qm8IaxpNX+gtkfZXLTTRAYaDJkCMycCSkpdg/cXqWypXKJtpRDW8qhLWXQjq7x2ATfMIxVv+6d/+OvIUV8fnVgCBAM1AcqGoYx+k8ef59hGNGGYUQnJyeTmppKcnIyx44d4/Tp08THx5OVlcXevXtxOp3ExsYCv11fNTY2FqfTyd69e8nKyiI+Pp7Tp09z7NgxLhwvISGBjIwM4uLiyMvLKziz+8IxLvx3165d5OTkcPDgQdLT00lMTOTEiROcOHGCxMRE0tPTOXjwIDk5OezateuSx9ixYwd5eXnExcWRkZFBQkICqampnDhxovS+pvh4cnr0YNczz9BofzIP9BhPSvJOvj4/ggHDasKd/ckLm853G08yfjzUq2cSFZXLs8+ms3ZtCqdOlcLX5GKnovzdi4uLE/eaJHYqyms6ceKEuNcksVNRXlNubq641ySxU1FeU2pqqrjXJLHTlV7T778npbym4nQqKluvomMYxlouvwd/OHCdaZr3/Pr7u4Aupmk+dKXjSt6Df+zYMRo0aGD3MK7O9u0wezbbV8xhclg6C9pA5ZQoume8RMq+PmyLta6+ExICgwfDkCHQrRuUK2fzuD3MK1uqS9KWcmhLObSlDNqxsFK9B7+IEoEuhmEEGIZhAP2AfTaPyXYBAQF2D+HqhYfDm28SvvMEnwxfwM5d3eicuYUVIf3IurYSS195nXennqdpU5g2DXr2hDp1YOxY+OwzyMiw+wV4hle2VJekLeXQlnJoSxm0o2vsukzmzYZhJAFdgS8Nw/jm14/XNwzjKwDTNDcDi4BYYNevY/3AjvGWJqdOnbJ7CK7z84PbbiP0sx9Y+eJPfOEYzTlnDjdn/I0jqyrzedP7SF0Zy6cLTAYNgmXL4JZboGZNuOEG+OAD+Plnu1+E+3h1S1WItpRDW8qhLWXQjq7RG115maysLCpUqGD3MNwmPSuNJz66kw+SlxOZbLD4E5PGjUNh3DhyR4zmh/01WboUli6FI0fAMKB7dxg2zPrVpIndr8B10lqWZdpSDm0ph7aUQTsWJmGLjrqEI0eO2D0Et6pSoRrv3/cFS0Ys4UCTykT8tSKrG56Hv/wF3yb16f3OcN4Y8DXxB/LZtQuefx7OnoW//tW6eE+nTjB5Mhw8aPcruXrSWpZl2lIObSmHtpRBO7pGV/C9jNPpxCH0rlEHTh5g2IJh7D+5n1kRL3Dnd6kwdy6kpkKDBjBmDNx9NzRvzqFD1v78xYthyxbr+e3aWVt6brnF+t+GYe/ruRLJLcsabSmHtpRDW8qgHQvTFXyhtm/fbvcQPKZFYAs23rORnk16clf0M7x2S304dsyaxYeHW0v1ISHQqxfNN87l7w+fY/NmSEyEN9+EGjXghRcgLAxatoSnnoKtW6G0voeV3LKs0ZZyaEs5tKUM2tE1uoKvSp2cvBzuXHInC/cu5KV+L/HUNU9Znzh+3FrRnzXL2pNTuTKMHAnjxkHnzmAYpKTA559bq/vffQd5edC4Mdx6K4wYYW3pKe0r+0oppZRSl6Ir+EJduDGCZH4+fsy/ZT6jQkfx9OqnmfrjVOsT9etby/L798P69dZenHnzoGtXaNsWpkyhDincfz988411p9w5c6wV/bfest4DNGtmHWLbNvtX9stCy7JCW8qhLeXQljJoR9foCr4qtfKceYxcNJLF+xYzc/BMxnUYd/GDzp6FTz+1VvU3bgQfH7jxRmtVf9Ag6/dAWpq1sr9gAaxaZa3sh4RYq/ojRlh79pVSSimlSjNdwRfqwi2XywIfhw8f3/IxA5sN5P7l97P68OqLH1S5MtxzD/zwA+zbZ11eZ9Mm65a4jRrBk09CXBzVqlk3zvr6a+ta+h98YG3d+c9/IDTU+gHACy9YPxwoKWWppXTaUg5tKYe2lEE7ukZX8L1MWTybPD0nne6zunP0zFE23bOJ1rVa//kTcnOtmfysWbB8OeTnQ7du1huB4cOtNwW/SkmxzuH95BPYsMHattO+/W8r+02beu51lcWWUmlLObSlHNpSBu1YmK7gCxUXF2f3EEpcFb8qLL99OX4+ftw4/0ZOZ53+8yf4+lor+J9/DklJ8OqrcOqUNcGvV8/avvPrbL5OHXjoIVi3Do4ehTfegAoV4JlnrP36nTrBa69ZF/Nxt7LYUiptKYe2lENbyqAdXaMr+F6mLN/RbePRjfSa04sbQm5gyYglGFdzORzThM2bYeZMa7k+I8PahD9uHNx1l3UC7+/89JO1tX/BAoiJsa6806cPjBplndtbtWrxX09ZbimNtpRDW8qhLWXQjoXpCr5Qx48ft3sItunWqBuvXPsKS/cv5fVNr1/dkw0DunSB//7X2oQ/Z461mv/009Ze/ZtugiVL4Px5AJo0gSeegOhoa1/+c89Z19u/5x6oU8e67OZnn0FOjuuvpyy3lEZbyqEt5dCWMmhH1+gKvpc5ffo01atXt3sYtjFNk1sX3srSuKWsu3sd3Rp1K94BDx60Jvtz5ljX2a9VC+6801rZb9v2D1/bunHWvHnWDwFOnLBW8m+91VrZ79ULrmabYFlvKYm2lENbyqEtZdCOhekKvlCZmZl2D8FWhmEwa/AsGldtzJ1L7iTjfEbxDhgSApMmWXtyvvwSeva0Lprfrp114fwPPoAzZ3792hAVZd0199gxWLHC2uq/YAH07WtdleeJJ2D79qJdY7+st5REW8qhLeXQljJoR9foBN/L6JnkUNW/Kh8O/ZAjp48wYeUE9xzUxweuvx4WLbJm72+8AZmZcP/91laeu+6CtWvB6Sx4+MCB1o11U1Jg/nzo0AGmTrX+266ddQnOhITLf0ltKYe2lENbyqEtZdCOrtE/NS/j6+tr9xBKhR5NejCh2wTej3mfrw9+7d6D16oFjz8OO3fCli0wZgwsXWqdZRsSAi++aF2d51cBATByJHzxhbW9/733oEYNePZZCA6Ga66xPnbqVOEvoy3l0JZyaEs5tKUM2tE1OsH3MhkZxdySIsgLfV6gba223LPsHk5mnnT/FzAM6zqZ770Hycnwv/9BUBBMnGjtxxk0CBYuLHSmbWAgPPAArF8PR45Yq/hpadalOOvVs67As2yZdal+bSmHtpRDW8qhLWXQjq7RCb6XqVmzpt1DKDX8ffz56OaP+CXzF5749gnPfrGAAOtM2tWr4fBha5K/Zw/cdpt1ic3HHoMdOwo9JSjIukjPrl0QGwsPP2xdfn/IEOspr7xSn5iYou3XV6Wbfl/KoS3l0JYyaEfX6ATfyyT9bmuIgg71OjCh6wRmb5/N2oS1JfNFg4PhX/+ylui/+Qb694fp0yE8HCIj4d134fRvN+MyDGtf/uuvWzt7li+3TsqdOdOHyEhrv/7LL3vmZlqqZOj3pRzaUg5tKYN2dI1eJtPL5OXl4ePjY/cwSpXM3ExC3wvFx+HDzgd24ufjV/KDOHUKPv7YupHW9u3g5wfDhlmX2+zb95LXz/zllzyWLPFh7lz44QfrjcC111rn8958M1SsWPIvQ7lGvy/l0JZyaEsZtGNheplMofbs2WP3EEqdAN8A3rvhPQ6cPMBLG16yZxA1asAjj8C2bdZ+nHvvta6j2b8/NG0Kzz9vXYrzd44f38N991nbdg4etHb9HDxoXYa/bl24+25Ys6bgwj2qFNPvSzm0pRzaUgbt6BpdwVdijPpsFIv2LmLHAztoVbOV3cOB7Gzr6juzZsG331of69fPWtUfOhQucettp9Oa8M+dC59+CmfPWufz3nmn9atlyxJ+DUoppZQqNXQFX6iYmBi7h1BqvTHwDSr6VuShLx+iVLxx9feHESOsffpHjlir+IcOwR13QP36nLjtNv54lq3DYd1ra8YM65KbH38MbdrASy9Bq1bQpculL7mp7KXfl3JoSzm0pQza0TW6gq9EeXfruzz81cMsGr6IW9rcYvdwLuZ0WjfMmjULFi+2VvnDwuCee6yr9AQGXvJpx49bk/0PP4Tdu6F8eWuf/t13W/v2y5Ur2ZehlFJKqZKnK/hC6TvZP3dfx/sIrR3K31b+jazcLLuHczGHwzrp9n//Y/vXX1vL8eXLW5fZrF/fuuzmihWQn1/oafXrw4QJ1r23YmOtG+x++y1cd511Oc5//MP64YCyh35fyqEt5dCWMmhH1+gKvhJnbcJa+nzYhxd6v8DEXhPtHk7R7NwJs2fDRx/ByZPQsKF1B92774ZmzS75lJwc66ZZs2dbu4CcTmt7z913w623QqVKJfwalFJKKeVRuoIv1K5du+weQqnXO6g3t7a5lZc2vMTRM0ftHs5lFWoZFgZvvGHtxVm0yPr9Sy9B8+bQp4818c/MLPR8Pz8YPhy++goSE6275iYnWxP8evWsXT8bNuiNtEqCfl/KoS3l0JYyaEfX6Aq+l8nJycHPz4brvHuZn9J+otU7rRjaaijzb5lv93Au6Yotjx2zNt3PmgXx8VClCowcaV2FJyrKunD+H5imdU392bOtq/BkZEBIiDXpv+suaNDAgy+oDNPvSzm0pRzaUgbtWJiu4AuVmJho9xC8QpNqTfh7t7/zye5PWP/TeruHc0lXbNmgATzzjHVx/O+/t86q/d//rEvphIZat8Y9caLQUwwDrrnGut9WcrI10a9XzzpM48Zw/fWwcKG1vUe5j35fyqEt5dCWMmhH1+gKvpdJT0+nSpUqdg/DK2TmZtLq7VYEBgQSfW805Ryl61IzLrVMT7eW5mfNgk2bwMcHbrrJWtW/7jrr95dw6BDMmWP9QCApybov16hR1sp+hw7Ffy1lnX5fyqEt5dCWMmjHwnQFX6i0tDS7h+A1AnwDeLX/q2z/eTszt820ezgXcalllSowfjxs3Ah79sDjj1t7cm66yVqif/ppOHDgoqc1bw4vvggJCdYJuf37wwcfQESENcGfNs06t1e5Rr8v5dCWcmhLGbSja3SC72X8/f3tHoJXua3tbfRs0pNnv3uWtOzS9Y9EsVu2aQOvvmotyX/+OXTqZP2+ZUvo0cPan5ORUegp5crBgAHwySfWFp533rE+9thj1lae4cMveZVOdQX6fSmHtpRDW8qgHV2jE3wlmmEYTB04lZOZJ/n39/+2ezie4esLQ4bA0qXWZP/ll+GXX6xtO3XrWpfT2bjxosvpVK8ODz0E0dHWVTofecS6B9egQRAcbN14V7c+KqWUUt5HJ/heJjs72+4heJ0O9TowrsM4pm2ZxoGTF29fsYtHWtatC3//O+zbZ23dGTnS2rPfvTu0bg2vvGIt3f/BhXN2jx2zTsJt0wZeeMG6idagQdZNd8+fd/9wpdDvSzm0pRzaUgbt6Bqd4HuZatWq2T0ErzSp7yQq+FRgwsoJdg+lgEdbGgZ06wYzZlgT+lmzoFYtePJJaNQIBg+2tvXk5hZ6Wvny1k2yVqyAI0dg4kTYvdv6WMOG8MQTEBfnuWF7K/2+lENbyqEtZdCOrrniBN8wjI4lMRBVNCkpKXYPwSvVqVSHZ3s8yxcHvuDb+G/tHg5Qgi0rVbIul7N+Pezfb83So6Oty25emLXv3XvR05o0gX/9yzox96uvrG39U6daPwjo0cO6Is+5cyXzEko7/b6UQ1vKoS1l0I6uueJlMg3DmAbUB6aYpvnjrx973TTNv5bA+Fwi+TKZesMH1+Xk5dDm3TZU8KnA9ge24+O49CUlS2w8drbMy7MupzNrFixbZv2+Sxdr3/6IEdbVei4hJQXmzrV+MHDggPWwO+6wLuwTEXHJe2+VCfp9KYe2lENbyqAdC3PnZTJPAJHAZ4Zh7DcM4whQt7gDVK45cIlLIKqi8fPx49X+r7Lnlz38N+a/dg/H3pY+PnDDDdbm+mPH4LXXrGvs33eftY9/zBhYt+6iE3Pr1Pltm866dTB0qLWSHxlpTfDfeQdOn7bpNdlIvy/l0JZyaEsZtKNrirKCHwe0N00zxzCM+sBLwDbTNKeWxABdIXkFXxWPaZr0nduXXSm7OPh/B6leobrdQyo9TBO2brVW9efPtyb8zZtb23vGjLHurHsJaWnWw2fMgNhY8Pe39uyPHw89e5bdVX2llFLK3dy5gn8UCAYwTfO4aZpjgPuLOT7lopiYGLuH4NUMw+CNgW9wKusU/15n72UzS11Lw4CoKJg+3Toxd+5ca4/+s89aN9G6/npYtAhycgo9rVo1ePBBiImxfo0bB198Ab17W5fkf/ll+Plne15SSSl1LZXLtKUc2lIG7eiaoqzgtwEWArG//moI9CrKuwe76Aq+upL7vriP2dtns/vB3bSs2dLu4ZRu8fEwZ471KykJAgPhzjutmXxo6CWfkplp7f6ZMcPaylOunHWz3fHjYeBAa4eQUkoppa6O21bwTdPcC0QAnwAVgZ+BIcUeoXKJvpN1j3/3+TcVfCrw15V/5Upvcj3Fa1o2awb//rd1OZ0VK6BfP3j3XQgLs+6e+9571j6d3wkIsN4DfP+9tV//b3+z7rV1443WtfUnTrQOJ4XXtFRXpC3l0JYyaEfXXHEF3xvpCr4qitc2vsaEbyewdORSBrccbPdwvMvJkzBvHsycad0G198fhg2z7prbuzc4Ll47OH8eli+3VvVXrLA+NmAA3H+/NfH39S3Zl6CUUkp5G3fuwVelyI4dO+weghiPdn6UtrXa8ujXj5KZm1niX9+rWwYGwqOPwvbt1sb7e+6xLpbfr5+14v/CC5CYWOgp5ctb7wG++gp++gmee866idawYdYW/2ef9d5Vfa9uqQrRlnJoSxm0o2t0Bd/L5OXl4aMbmN3m+4Tv6f1hb57t8Swv9n2xRL+2uJbZ2dbdcWfOhNWrrY9de621V3/oUGuV/w/y8qzV/Pfftyb+pmmt6t93n7Vn31tW9cW1LMO0pRzaUgbtWJiu4At16NAhu4cgSq+gXowOG82rG1/lwMmSvdauuJb+/jByJHz7LRw+DP/8p3U3rNtvh/r14f/+D7ZtK/QUHx9re84XX1ir9//8J+zZA7fc8tuq/pEj9rycqyGuZRmmLeXQljJoR9foCr6XycjIoFKlSnYPQ5SfM36m5dst6dygM9+M/gajhC7cXiZaOp2wZo11bf3Fi61LbIaHW6v6d9xhbfX5gwur+h98AF9+aa3q9+9v7dUvrav6ZaJlGaEt5dCWMmjHwnQFX6jU1FS7hyBO3Up1ebHPi3x7+Fs+2f1JiX3dMtHS4bD25c+bZ11b/513rGtmPvqotao/ciSsXAn5+QVPubCqv2zZb6v6e/daq/qNGsEzz5S+Vf0y0bKM0JZyaEsZtKNrdILvZfRdrGc81OkhohpE8eiKR/nl3C8l8jXLXMvq1eGhhyA62jo598EHre08AwdCcLB11u3hw4We0qiRNcFPSLC28URFWTfOatrUetrixZCba8/L+b0y11IwbSmHtpRBO7pGJ/heJrc0zGYEKucox6zBsziTfYbHVjxWIl+zTLds3x6mToXjx+HTT6FtW3jxResKPBdW/LOyCh5ertxvq/o//QTPP2+t6t9662+r+n94b1CiynRLYbSlHNpSBu3oGp3gexmn02n3EMRqW7st/+j5D+bvns8X+7/w+NfTloCfHwwfDl9/bc3cX3zRWq4fPRrq1bNW+bdutTbi/6phw99W9Zcvh86drVX9Zs2sK/DYsaqvLeXQlnJoSxm0o2v0JFsvc/r0aapXr273MMQ6n3+eyA8iOZl1kl0P7qJGhRoe+1qSWzpNJ5m5mZw7f45zuefId+ZjGAYOw4HDcODv408VvypU8Klw8UnNTiesX29dbnPRImslv10768Tc0aOhVq2Lvl5SknUe74wZcPQo1KkDd98N995rbefxNMktyxptKYe2lEE7FlbUk2x1gu9l4uPjadasmd3DEC02OZbOMzoztNVQPr31U49dVcebW6ZmpnLg5IGCX0fSjvBzxs8Fv9Ky04p0nHJGOar4VaFGhRrUr1yfBlUaUL+S9d9GVRoRUr4uLb7bQcDs/8HmzdYldAYPtib7AwZYZ+T+Tn7+b1fgWb7ceq/Qv791Xf0hQzx3BR5vbqkK05ZyaEsZtGNhOsEXOsHPysqiQoUKdno9nCkAACAASURBVA9DvJc3vMxTq59i5uCZjOswziNfw1tanjt/jo1HN7L52GZikmOIPh5NUnpSwed9HD40qdqEepXrUbdSXepVqkeNCjWo6FuRiuUrUtG3Ij4OH5ymE6fpxMQkKzeL9Jz0gl+pWakcP3ucY+nHOHb2GNl52YXG0KhKI1r5N6RlUhZhPxwi4kAG7crVw+/OsdZSfUjIReNOSoLZs+G//y28qj9+vLWdx528paW6Mm0ph7aUQTsWphN8oRP8vXv30qZNG7uHIZ7TdHLt3GvZcmwLsffH0iKwhdu/RmltmefMY9PRTaw+sprvjnzHj0k/kuu0NrW3DGxJx/odiagbQetarWkR2IKgakH4ONx3l0HTNEnLTiPxTCIHTh5g/8n9xKXGsf/kfvan7ufs+bMA+DoN2qWYRCRDZEBzuvcdQ9vbH8NRqXKh4+XnwzffWHfL/eOq/uDBUL588cdcWluqq6ct5dCWMmjHwnSCL3SC73Q6cTj03OiSkJSeRPvp7WlQuQGb7tlExfIV3Xr80tQyKzeLbw9/y5K4JXyx/wtOZp3EwKBj/Y70DepL3+C+dG3UlSp+VWwdp2maHD59mNjkWGKTY4lJ2EjssWhOkglA1WzoZjbkmtAbuKb77UQ17Iy/j3/B848d+22vfmKitap/zz3WXv2gINfHVZpaquLRlnJoSxm0Y2E6wRc6wY+NjSUiIsLuYZQZ3xz6hkHzBjGi3Qg+HvaxW/fj293SaTpZc2QNH+74kM/2fca53HNU9avKjS1uZGirofQL7kf1CqX/xCbTNDl8Kp4fvpvDhi0L2ZB9kH01rX/X/PHhmgbd6N/6Bvo37U/7uu1xGI6LVvVNEwYNggcegOuvty7LeTXsbqncR1vKoS1l0I6F6QRf6ARflbyX1r/EM989w5T+U/hbt7/ZPZxiO3jyIB/u+JC5O+ZyNP0oVf2qclvb27i1za30DupN+XJu2LNip7NnOfnJbH746j3WZMWxsjns/fXCO7UCatGvaT/6N+3PwGYDaVClAYmJ1or+jBnWzXYbNbK279xzj3WlTqWUUqq00Am+0Al+TEwMHTt2tHsYZYppmgxfOJwlcUv47LbPGNJqiFuOW5It8535fHnwS97e8jbfHv4Wh+FgQLMBjG0/lsEtB1PBV+gJTPv3w+zZHFs0i9WVfuHbNv58G+IgxWFt6YmoF8HgFoO5qeVNtAvswPLlBtOnWzfY9fGxrrzz4IPQpw/82U+I9ftSDm0ph7aUQTsWphN8oRN8ZY+M8xn0/bAvu07sYvVdq+nWqJvdQyqS1MxUZsbO5L3o9/jpzE80qNyAByIfYFyHcdSvXN/u4ZWcvDzrZlqzZmEu/4Jdgfl81a8xX7TzZdP5w5iYNKzSkBtDbmRwy8E0yuvLh7P8mD0bTp60LtJz//0wdiwEBtr9YpRSSpVVOsEXOsHXvWj2+eXcL3Sb1Y1TWadYfddqwuuGF+t4nmy5M2Unb/74JvN2zSMnP4c+QX14uNPDDGk1xK1XvPFKKSnwv/9ZN9Lat48TNSvw1cgIlrV2sDItlnO556joW5GBzQdyU9Ph5O0ewoczK7Bhw2833n3wQejaFS6ckqHfl3JoSzm0pQzasTCd4Aud4OvZ5PY6cvoIveb0IuN8BivvXElk/St+j12Wu1vmO/NZtn8Zb25+k+9/+p4A3wDuCruLR6IeoW3ttm77OmKYpnXzrFmz4JNP4OxZsls2Y82d1/BFiMnSpFUcP3scv3J+XNf8Ojr7jufIqgF8Mq88Z89CaKh1Uu7o0VCpkn5fSqH/xsqhLWXQjoXpBF/oBF+vB2u/hLQE+nzYh1NZp1g2chm9gnq5dBx3tUzLTmNm7Eze3vo2CWkJNK7amEc6PcL4iPFecRWcUuHcOVi82Jrsf/89OBw4B13HphHdWVgjmUX7l3Ds7DHKlytPvwZDqHvkL8R80Ymd232oWBEGDTrNM89Up0MHu1+IKi79N1YObSmDdixMJ/hCJ/h6R7fS4eiZowz43wAOnTrEO9e/w30d77vqYxS35f7U/UzbPI0Pd3zIudxz9Gjcg8c6P6bbcIrr0CHrFrhz5sDx41CzJs47R/PjkI4syo5l0d5FHE0/io/hSxfHI/jEPsLmFcFkZRlERVmr+iNGQECA3S9EuUL/jZVDW8qgHQvTCb7QCX58fDzNmjWzexgKOJN9hpGLR7Li0ArGdxjPG9e9QaXylYr8fFdaOk0nK+NX8ubmN1lxaAXly5XnjtA7eDTqUTrU0+Vjt8rPh5UrrVX9pUshNxeionDePZYtvUNY+NPXfLr3U5LSkwjIrU/bnyeTsvZmEuMrUa0ajBljnZjburXdL0RdDf03Vg5tKYN2LEwn+EIn+KdPn6Z6dd12UVrkO/OZuGYikzdMJrh6MO/d8B4Dmg0o0nOvpmXG+Qzm7pjLW1veIi41jrqV6vJQ5EPcH3k/tSvWLs5LUEWRmgrz5lkn5u7aBf7+cOutOO8ey7ogg1mxc/gi/gvSstKodmIwtfc+x+GNHcjLddCrl7Wqf/PN1km6qnTTf2Pl0JYyaMfCdIIvdIJ/7NgxGjRoYPcw1B+s/2k9dy+9m/jT8dwQcgP/7PVPOjXo9KfPuVLLPGcea46sYcGeBSzau4gzOWfoVL8Tj3V+jOFth3v/Dam8kWlCTIy1qv/xx3DmDAQHk37LLfg9fD9fZe1i3q55LD+wnJwzVQg8MAFn9HhOH69BrVrWzbPuuw+Cg+1+Iepy9N9YObSlDNqxMJ3gC53gJycnU09vr1kq5eTlMG3zNCatn8SZnDNc0/gaRoeO5ubWN19ylf1SLZPPJrP6yGpWHV7Flwe/JDUzlcrlKzO01VAejHyQLg27YFy4NqOyV1YWLFliTfZXr7aumTlgAIwbx5mBvVl8eDnzds3ju/i1cPhaqu1+mjM7e4JpMHCgwYMPwvXXWzfUUqWH/hsrh7aUQTsWphN8oRP81NRUatasafcw1J9Iz0lnZuxMpsdM58DJAwC0qtmKyPqRBFcLpm6lupQvV570s+lkO7I5ce4E+0/uZ8fPO0jOSAYgsEIgA5oN4La2t3Fd8+vw9/G38yWpKzgVE0ONZcusk3OPHoUaNWDUKBg3jmNNa/HJ7k+Yt2se2/afwLHtfsrv+H/27js6qmrvw/hzUiCNDiIdpEhPQpMqVcEGCKigKNVyrXjt1wLqtaJe7Fjo2FEUEVTACqhAAiECoURi6BAghJBCktnvHwFeopQwTnIym+9nrSwkmUx+k4eDO5szZ24la38latY03Hijw6hRoA2qkkF/x9pDLe2gjgVpgW/pAj8pKYm6deu6PYYUgjGG1btW89XGr/hl6y+s2rmK7Qe34zGeAreLKBVBg4oNaFm1JZFVI+lWtxtR50YR4Oi6v/7i2HGZlwfffZe/qz97NmRnQ3R0/rk5117L6sNbmLZqGjNWfcCele0otfJODm/oQWCgoW/f/F39nj1Bl3x2j/6OtYda2kEdC9IC39IFfnp6OhERhb9Si5QsOXk57M3cS05eDpkZmdSuUlu78xY44XG5bx988EH+Yj82Nv8ZtldeCSNHktPtQr7+41umxk1lzi9ryF0+gqC4m8hNr0C98/L41y2BDB8OVaq48nDOavo71h5qaQd1LKiwC3ztE/mZrVu3uj2C/APBgcGcG3EutcrVwnPAo8W9JU54XFasCLfdlv+k3JUr859d+803cPHFBDdoxBXvr+DTdi+y46nFvPxiKM2e6QMDh5CUt4T774fqNfIYcq2HxYvzn9srxUN/x9pDLe2gjt7RDr6fyc3NJUjPyrOCWtqj0C2zsmDOnPxd/W+/zV+59+wJI0fClVcSd2AD0+KmMfXb5exffBXO6mGYrHI0aJzFXbeFcP31UK5c0T+es5mOS3uopR3UsSDt4FtqzZo1bo8gPqKW9ih0y5AQuPpq+PprSEqCJ5+EP/7If0JutWpEPvEWL1W6ll3PLeKL6XW5fOItBPS/kU1p8dxxB5xzbg4jRuUQE1OkD+espuPSHmppB3X0jis7+I7jjAeuAA4DicAIY0zqCW7XB3gZCATeNcY8W5j7t3kHX0Qs4/HAjz/m7+rPmpW/y9+yZf6u/nXXsSfUMGP1DF6ZvZQ/F/WG+GshJ5wmkYf49+1hDBniEB7u9oMQEZHiUNJ38BcAzY0xLYENwEN/vYHjOIHA68AlQFNgiOM4TYt1yhIoRlt31lBLe/yjlgEB0L07zJgBO3bAxIn5T8gdMwaqV6fK8Fv5d2oTNj/9IYtnN+XaqfcRfMXdrNu+mRtvdKhybjY33ZqFNrl8Q8elPdTSDuroHdfPwXcc50pgkDHmur+8vwMwzhjT+8jvHwIwxjxzuvvUDr6I+L34+Pzr6s+YASkp+RfKHz4chg/nQM0qfBD/IS9/soKEr7vC2qsgrzQt2qbywJiyDBoYQOnSbj8AERHxtZK+g3+8kcD8E7y/BrDluN9vPfK+E3Ic5ybHcVY4jrNix44dpKSksGPHDrZt28b+/ftJTEwkMzOTtWvX4vF4iI2NBf7/J8PY2Fg8Hg9r164lMzOTxMRE9u/fz7Zt2zh6f0lJSaSnp5OQkEBubi5xcXEF7uPor/Hx8WRnZ7Nx40bS0tJITk5m9+7d7N69m+TkZNLS0ti4cSPZ2dnEx8ef8D7i4uLIzc0lISGB9PR0kpKSSElJYfHixdY9Jhs7FeYxLViwwLrHZGOnwjymxYsX+/4xhYSQ/cwz/P7NN/Dpp6TWqwfPPAMNG1KuTz8u/WYP8Q+/wOy3Ihg29WFKX/II8ZtSGHpdABXOSWfUv1L47rs/1ekMH9PRN5sek42dCvOYlixZYt1jsrHT6R7T8cekLY/pn3QqrCLbwXccZyFw7gk+9LAx5osjt3kYaAMMMH8ZxHGcq4DexpjRR35/PdDOGHPH6b62dvBFxErbtsH06fnn62/aBGXKwJAhMHIkmdEtmLV2Ni+8t5LVX3WA9f3AE0RUp9088u9K9OsbiC5EISLi31zfwTfG9DLGND/B29HF/TDgcuC6vy7uj9gK1Dru9zWB7UU1r784+hOg+D+1tEextaxRAx56CDZsgJ9+goEDYeZMaN+e0Oi2XL9oJ3H33seGn1ty23vPE3Hx86yKz2bQwEAqnHuAOx/Yhy4pfWo6Lu2hlnZQR++4dRWdPsBLQFdjzJ6T3CaI/Cfg9gS2AcuBa40xp30qmc07+NnZ2ZTWybVWUEt7uNry4EH4+GOYNAl++QWCguDyy/NfMffiXszdtJCnp6xixZw2sLE3OIY23Xcy9p5zuLRPMAEl4UTNEkTHpT3U0g7qWJDrO/in8RpQBljgOM4qx3EmAjiOU91xnHkAxphc4HbgG2Ad8HFhFve2S05OdnsE8RG1tIerLcuUgVGjYOlSWLsW7r47f6Hfty/Bdc/jyhlLWH7j1Wxd0ZJ/f/AmZXu8xYpfg7nismAq1dzL/WP3sueE2yxnJx2X9lBLO6ijd1y/ik5RsHkHPy0tjbJly7o9hviAWtqjxLXMyYH58/PP1Z87F/LyoFMnGDmSvIED+Grrrzz5djwr5rSFpG44gTl06r2Dx++tTvduQTiO2w/APSWupXhNLe2gjgWV9B188VJq6t9eD0z8lFrao8S1DA6Gvn3h889h61Z4/nnYuxdGjSKwRk36vvgxy6/uyNZVDbh92huEd5zO4u/K0LNHEFXq7mbsc3spaQ+puJS4luI1tbSDOnpHC3w/ExIS4vYI4iNqaY8S3fLcc+G++/JP31m6NP+qO598Ap07U6NdT17dcZDUD3rzya+/EHnzBPbmbeaJBytRqWoWvQb+ya/Lct1+BMWqRLeUM6KWdlBH72iBLyJyNnAc6NAB3nkHdu6EqVPzF/8PPkhgnboMeuRNVvWuQ/Kqytw4cSIhrWax6MtKdLggiOrnb+O5V/dy6JDbD0JERApDC3w/k5WV5fYI4iNqaQ+/axkeDsOGwY8/5l9y8/77ISYGBgygVtMOvL1xEwfeacl7S36k6bDX2ZG6jwfvrET5Koe4fOhmVsfnuf0IiozftZSTUks7qKN3tMD3M+XLl3d7BPERtbSHX7ds2BCefhqSk+Grr+DCC+GVVwhqEcm1dzzJmo7B/LHMcMPLbxPUZD5ffVidyJaB1In8k1cn7SU72+0H4Ft+3VIKUEs7qKN3tMD3M7t27XJ7BPERtbSHFS2DguDSS2HWrPxXzH3pJUhPh5tvpl6T9kxbsZi0Fyow5cdvaTT4HZK35HDn6EqUrXKAQTclsmGjHbv6VrQUQC1toY7e0WUy/Yxe8MEeamkPa1saAytW5F9u8/33IS0N6teHESPY1K8bj3yVwOczq5K9pg+YIBq0TeShuytww1UVCQpye3jvWNvyLKSWdlDHgnSZTEtt2LDB7RHER9TSHta2dBxo2xbefBN27IAZM6B2bXjkERpEXsiHP8wi7dGDTPz6S+pdOZVNCaUZdW1FylTdy/V3bWLLVo/bj+CMWdvyLKSWdlBH72gHX0REzswff+RfhWfqVNiyBSpVgqFDWTegF//5PoWvPqhFzvruEOChaadEHrvnHK66ogIB2lISEflHtINvqZiYGLdHEB9RS3ucdS3POw+eeAI2b4ZvvoGePeHNN2nS9Qpmf/kaB+/4nQmffkKtPh+zNqYCg/tXoGz1ndz4wEZ27S7Zu/pnXUuLqaUd1NE72sEXEZF/bu/e/PP0J02CuDgICYEBA4gf2JsHlzl8+/F55G7uBIGHieqxiSfurc7lF5XHcdweXETEf2gH31L6SdYeamkPtST/NJ077oBVqyA2FkaPhvnzaTFwGF99+CgHh83nuUkzqN5tDqt+qk7f3uUpX2cLdz6+gdTUkrPRpJb2UEs7qKN3tIMvIiJFIysLPv88/yo8Cxfmv69nT1YMuoyH1lbm+8+akre1FU6pDNr1TuSpB2rRs5OueS0icjLawbdUXFyc2yOIj6ilPdTyJEJCYPBg+Pbb/PP1x42DTZtoc8vdLJh+Bwf7vc24F96hygUL+e3r8+jVuTyVGyby4PgNHDrkzuaTWtpDLe2gjt7RDr6fyc3NJchfLzAtBailPdTyDHg88MMP+bv6n36av8sfGcmSq/vznz8asfjLaDy7mxAQmkbnKxJ59oHz6NCqXLGNp5b2UEs7qGNB2sG31KZNm9weQXxELe2hlmcgIAB69ICZM/Ovrf/GGxAcTKeHH+fHGSM40O0RHnz4NSq0WMpPnzalY+tynNt8HU+8vp6srKLfkFJLe6ilHdTRO9rB9zPp6elERES4PYb4gFraQy19YPVqmDIl/8W09u6FmjX57qpBPLyzHb990x6zrx6BEXvpMSCJ8Q81JLJx2SIZQy3toZZ2UMeCtINvqZSUFLdHEB9RS3uopQ+0bAn/+x9s2wazZkGLFvR4+RV++eBaUqOGcddt/6NM/dUsmBFFVNMIarZezfjJ68nJ8e0mlVraQy3toI7e0QLfz+inWHuopT3U0odKl4aBA2HePPjzT3jqKcr+uZ0Jr/+b/Zv7M++GW2h1xcds31iZ+0edT3jVXVxxyzLWbz7oky+vlvZQSzuoo3e0wPczOTk5bo8gPqKW9lDLIlKzJvznP7BxI/z4I/TvzyUfv0fMnCHsrXcxNw8dT0jVROa+1Y7GDUKp1zGG1z9cj+cfvFiuWtpDLe2gjt7RAt/PeP7J/7mkRFFLe6hlEXMcuPBCmDYNdu6Et9+mQlgZJs68nwObujKr73W06PEZSXF1uH3I+YRVS2bQmF9J2n7mu/pqaQ+1tIM6ekcLfD8TFhbm9gjiI2ppD7UsRmXLwo03wi+/wJo1OGPuZuCvC1m98Bp2Vm7NsH7PEhixm09fbk+9OsE06v4rU77YQGGvJ6GW9lBLO6ijd7TA9zP79u1zewTxEbW0h1q6pGlTGD8etm6F2bOpGhnJ1LmPkP5HW2ZeOJDG7b5i4y9NGdm/EeG1NjL0waXsSDl0yrtUS3uopR3U0Tta4PuZ6tWruz2C+Iha2kMtXRYcDP37w5w5sGULznPPcd3O31m3dBDJ4Q0Z3PNpDFm891xHqleHpr2X8ME3G094V2ppD7W0gzp6Rwt8P7N582a3RxAfUUt7qGUJUq0a3H8/JCTA4sXU6n85H/z6NIe2teTtyCuo33IB676P4to+DYmok8DwR5eya1/GsU9XS3uopR3U0Tt6oSs/4/F4CAjQz2U2UEt7qGUJl54On3wCkyfD4sVsDi/PvZH3MPePgRze2QRKHaR5zzjG3lONAd3rqaUldFzaQR0L0gtdWWrVqlVujyA+opb2UMsSLiICRoyAn3+G9eupd8ctfLr5DTJ3NuX1hhdTr/FCfl/Qmqt61afMeWsY/fhSUlIz3Z5a/iEdl3ZQR+9oB19ERM4+ubnw9df5u/pffsmm4AjubXIX87cM4fCe86F0GpEXx/HEvTXpe2E9t6cVEQG0g2+tmJgYt0cQH1FLe6ilHwoKgssvh88+g23baPDfR/k86xMy9zTmf7V7Uvu874ib35Z+XetRrsHv3PLfX0g9mO321HIGdFzaQR29ox18ERERAGNg2bL8Xf0PPiAhL5h7693KtzuHkbO3AU5oKtG94/nv/bW4pENdt6cVkbOQdvAtFRsb6/YI4iNqaQ+1tITjEBscDG+9BTt30njiBOZWWUzm3oaMr96dGjV/IHbOBVzasS7lz1/NHc/9Stqhw25PLSeh49IO6ugd7eD7GT2b3B5qaQ+1tMcJWyYmwtSpMHUq8fuyua/mTSzaM5Lc/efhhO2lzSVreeq+Olx0QW1XZpYT03FpB3UsSDv4lkpISHB7BPERtbSHWtrjhC3r14cnn4SkJFp8Op2vIzeQkd6Yp6v3otq5P7N8dnsubl+bik1X8e8Xf+NQZk7xDy5/o+PSDuroHe3g+5nMzExCQ0PdHkN8QC3toZb2KHTLlBR4/32YNImVibu479zR/JAymrwDdQkIT6Hd5Wt59oHz6Bpds+iHlhPScWkHdSxIO/iW2r59u9sjiI+opT3U0h6Fblm5Mtx5J6xaRfSPX7Gw934ynWjG1ehDlcpL+fXjjnRrVZPKLWK5/+VlZGbnFu3g8jc6Lu2gjt7RAt/PVKxY0e0RxEfU0h5qaY8zbuk40Lo1vP46wdu3M3b8MHY2fJVl4XXoVu9RUpOrMH5MOyKq7KXz0B9Zsnpb0Qwuf6Pj0g7q6B0t8P1MRkaG2yOIj6ilPdTSHv+oZWgoDBkCCxbQNn4J398QyKHyXXi4xuVUKrecJe93pnNkNc6JXMHDry8n+3Ce7waXv9FxaQd19I4W+H5GzyS3h1raQy3t4bOWdevCuHGU3vwH/506ht1d3mdxxfPpXPdJ9v5Rjadvb0tYld10veFHfluzwzdfUwrQcWkHdfSOvmt+Jjg42O0RxEfU0h5qaQ+ftwwIgF694P336bRxOT/fV4X0Rv15oGY/KkSs4qcZXWjf4hzObbWMcW+v4HCOdvV9RcelHdTRO1rg+5n09HS3RxAfUUt7qKU9irRlhQpw662Exizn2S8fJ2XQ1/xYI4oOdZ5lz4ZaPH5zG8LO2UmPET+yfO3OopvjLKHj0g7q6B0t8P1M5cqV3R5BfEQt7aGW9ii2llFR8PLLXJi4nKXPNyKt8yj+XWsg5ULX8P3UrrRrXoWq0ct49M3lZB3WFXi8oePSDuroHS3w/czWrVvdHkF8RC3toZb2KPaWpUvDVVcR/vU8Xlwygb23LuO78zvToc6zpGysxX9vbUt4lT1ceP0PugLPGdJxaQd19I5e6MrP5ObmEhQU5PYY4gNqaQ+1tEeJaOnxwE8/kfHuFJ5YnMHknOHs2X4JAJWbxzDyRg9jb2pFWIjOTT6VEtFS/jF1LEgvdGWpNWvWuD2C+Iha2kMt7VEiWgYEQLduhM2cxrNx77J77DZ+bn85Xeo+yb6kajx/1wWUqbKPjkN+4IfYLW5PW2KViJbyj6mjd7SDLyIi4g9+/52sSVN4+qstvJ1xHbu2Xw4mkIqNV3DD6ByevLU1EaGl3J5SRIqQdvAtFRMT4/YI4iNqaQ+1tEeJbtm8OSH/e5Enfp/Jzlc9LOt9LT3qPsqB7ZWZcG8HylY5QLuB3/Htb0luT1oilOiWUmjq6B3t4IuIiPir7ds5PGUaz330O2/uH8CO7X3BE0z5BjFcNyqLp25vQ7mI0m5PKSI+oh18S+knWXuopT3U0h5+17J6dUo9/BCPxs1k+wdVWTX4di4+72HS95Tn9Yc6UeGcdFpdsYgvFye6PWmx87uWckLq6B3t4IuIiNjk4EFyP/yIFyf9wmvbLmbr9ivBU4qydWMZPPIQz9zVhoplQ92eUkS8oB18S8XHx7s9gviIWtpDLe1hRcsyZQi6cTQP/DqJLQsiif/XY1za6GEyUyN4+7EuVK6aRWTvb/n0hw1uT1qkrGgp6ugl7eD7mezsbEqX1vmUNlBLe6ilPaxtmZND3rx5vPLaQl7e0J4/tw2CvNJE1FrJVden8uy97TinQrjbU/qUtS3PMupYkHbwLZWcnOz2COIjamkPtbSHtS2Dgwns14+7F7xK0rJerH/kZfo1f5TD6aWZ8nR3qp6bR7NuX/PBt+vcntRnrG15llFH72iB72eqVq3q9gjiI2ppD7W0x1nRsmpVGo27n89XP0Hm3AO8duUDnFftC9Yu6cq1vZsQXiOOoXd/w/aUg25P+o+cFS3PAuroHS3w/UxqaqrbI4iPqKU91NIeZ1VLxyGgYwdu++w5EtcMIPF/7zOw9ePkZTm8N6E3Nao7NO44j2lfrMbj8b/Tec+qlhZTR+9oge9nQkJC3B5BfEQt7aGW9jhrW4aHc97to5i1YiwZS0N4e/gTNKz1KetXdGV4/5aEV1/DVTd9yebt/rPYOmtbWkYdvaMFvoiIiBwTcH4jbpzyGBs2DOXP6Qu4pvPTBORlMeudKzivdgj1oufz0uSl5OZ5Y3FsTAAAIABJREFU3B5VRE5CC3w/k5WV5fYI4iNqaQ+1tIdaHicwkNqD+/Phz//h0Lq6vHf3SzSvP5OkdR24Z1RHQqsk0fuqz1i1fofbk56QWtpBHb2jBb6fKV++vNsjiI+opT3U0h5qeRKVK3PtS/8mPmEUKd+s5eY+zxBeegvfzhpAdJMqVGu8iEefW0DW4Vy3Jz1GLe2gjt7RAt/P7Nq1y+0RxEfU0h5qaQ+1PA3HoVLXjkyc/xCpf7Tjm2cm0b7F2+ze0oz/PngR4ZV206n3LL77daPbk6qlJdTRO3qhKz+jF3ywh1raQy3toZbeyUjYwJPjvmDy0qbs3toHTCAV6i5myMC9PDWuN+Ujiv+JkmppB3UsSC90ZakNG+x+afGziVraQy3toZbeCWvciGc+vI9dm/uwfMqX9LrgVQ7uq84bL/ajYpUMIjt9yqwvY4t1JrW0gzp6Rzv4IiIi4nO5e1KYMPYDXv22GslJV0BeacKrxdK/zyaefeZialbVudUiZ0o7+JaKiYlxewTxEbW0h1raQy19J6hKZe594w7+3DSIDfOWMaDHK+RmB/PelKupVSuYhtFf8OY73xfZi2ippR3U0TvawRcREZFi4cnIZNqzH/L8rFIkJPaFw2UoVTGBi7vG8PwzXWlyfk23RxQp0bSDbyn9JGsPtbSHWtpDLYtWQFgoI54Ywbq117Fj+TZG9HuF0qX2M3f2dTRteg41G8/jv098wWEfXG5TLe2gjt7RDr6IiIi4x+NhzsTPeeLdNGITLsNkViGwTDKd2/3EU2Oj6NSludsTipQY2sG3VFxcnNsjiI+opT3U0h5q6YKAAPreOoAVscNJ3WS4e/hblC+/kR8XXUvnC5tSud533HPn+6QfzDiju1VLO6ijd7SD72dyc3MJCgpyewzxAbW0h1raQy1LjiVf/MzDL2xgcdxF5B2sjRO6h1Ytv+Wxf9em79VdTvv5amkHdSxIO/iW2rRpk9sjiI+opT3U0h5qWXJ06teFH34eRcaOKjx5z3Sq1/iNmOVX0++aLkRUW87QwVNI3rz9pJ+vlnZQR+9oB9/PpKenExER4fYY4gNqaQ+1tIdalmzrlq/joXFL+Pq3DmTvbQZBGZzX6BvuuM5w+/39CAoKPHZbtbSDOhakHXxLpaSkuD2C+Iha2kMt7aGWJVuTtk34/KvRZOxqwtQJX9C02Rz+SOzB3Q8PILRyEr16TWX5z6sBtbSFOnpHC3w/o59i7aGW9lBLe6ilfwgIDGDYXf1Ys2owexKzueWGSUSU2caiRcNpd2FzqtRbxPj/LiRt/0G3R5V/SMekd1xZ4DuOM95xnATHcVY7jjPbcZy/vV614zi1HMf53nGcdY7jrHEc5y43Zi1pcnJy3B5BfEQt7aGW9lBL/1O5xjm8OW0U+7dcyPdzltClyzT2723IG5NGU776YaLavs/HUxe6PaZ4Scekd9zawV8ANDfGtAQ2AA+d4Da5wD3GmCZAe+A2x3GaFuOMJZLH43F7BPERtbSHWtpDLf1btys68dNPI8jYVZWH73yHmrWXELdyINeM6EVY1ZVcPWAymxOS3R5TzoCOSe+4ssA3xnxrjDn6MnW/An97bWpjzA5jTOyR/z4IrANqFN+UJVNYWJjbI4iPqKU91NIeammHUqGluWfcIJLX92XTyq0M6vcuGIdPZo/kvObnULfJFzw/9mNyc/75K+ZK0dIx6Z2ScA7+SGD+qW7gOE5dIBr4rRjmKdH27dvn9gjiI2ppD7W0h1ra42jL+i3q88nno8nYFcn7E+fRIvIzkpO68MATVxNSaRvduk1l8TfLXZ5WTkbHpHeKbIHvOM5Cx3F+P8Fbv+Nu8zD5p+K8d4r7iQA+BcYYY9JOcbubHMdZ4TjOih07dpCSksKOHTvYtm0b+/fvJzExkczMTNauXYvH4yE2NhaAmJgYAGJjY/F4PKxdu5bMzEwSExPZv38/27Zt4+j9JSUlkZ6eTkJCArm5ucdeXe3ofRz9NT4+nuzsbDZu3EhaWhrJycns3r2b3bt3k5ycTFpaGhs3biQ7O5v4+PgT3kdcXBy5ubkkJCSQnp5OUlISKSkpBAUFWfeYbOxUmMe0d+9e6x6TjZ0K85iCgoKse0w2dirMY6pevbp1j8nGToV5TKVKlSr4mA4fpnnHWqyOuZbv5qzmztFTKFd+Ez/+eANd+rSlYu0fGT74dZI3by2xj8nGTqd7TMcfk7Y8pn/SqbBcuw6+4zjDgFuAnsaYE77+tOM4wcBc4BtjzEuFvW+br4O/du1amjY965+KYAW1tIda2kMt7VHYlr8sWM4jz8TzU0x3ctPqQelUmjX5mvtvLMPQmy8hILAknOxw9tIxWVBhr4PvygLfcZw+wEtAV2PMnpPcxgGmAfuMMWPO5P5tXuB7PB4CAvSXjQ3U0h5qaQ+1tMeZtszNyeWVpz/j1Q9LkbSpN+SGElJ5NZd0WM4zT3bl/MgGRTitnIyOyYJK+gtdvQaUARY4jrPKcZyJAI7jVHccZ96R23QCrgd6HLnNKsdxLnVp3hJj1apVbo8gPqKW9lBLe6ilPc60ZVBwEP8eezWb1/Unac0ehgycRGDgYWZ/OYrGrWpTs9FcnnjgfQ5nHS6iieVEdEx6x7VTdIqSzTv4IiIiUnw+nbaAp1/fxcrfe2MyqxAQsY22LRbx2Jg6XHp1V7fHk7NMSd/BFy8dfVKG+D+1tIda2kMt7eGrlgOHXUTMsqGk7QjlvtumU+Wc3/nt12u57JqulKm2jOuumsSfG3Rt/aKiY9I72sEXEREROQO/L1vHQ+OWsmBZe7L3NoPALGo3+IabBmRw32MDKRVSyu0RxVLawbfU0cs9if9TS3uopT3U0h5F2bJ5uyZ8OW8UGbuaMOON/Gvrb/mzM488M4TQKrvp2Gka82f9VGRf/2yiY9I72sH3M3o2uT3U0h5qaQ+1tEdxt0zbl8bjD89m5jfV2Z3UHUwQEdV+o1/n33n66Yup3aBWsc1iEx2TBWkH31IJCQlujyA+opb2UEt7qKU9irtl2YplefHNYez64yJWLkngsksmkZNdhvc+GUWdJpWp2+Rznn30Q12F5wzpmPSOdvD9TGZmJqGhoW6PIT6glvZQS3uopT1KQktPnocZb87jhcnprFnbG5NdgcAyW7ig5XeMu6cBF13ZydX5/EFJ6FiSaAffUtu3b3d7BPERtbSHWtpDLe1REloGBAYw7PbLiY8dzL6tDnffMo2KldazdOlQLh7QibLVf2HY4Mls/WOb26OWWCWhoz/SAt/PVKxY0e0RxEfU0h5qaQ+1tEdJa1m+cnleenMYuzf3IuantVzSezLZ2RWY/tFIap1fgfOazmb82I/Izcl1e9QSpaR19Bda4PuZjIwMt0cQH1FLe6ilPdTSHiW5ZavOLZj39Ugydzfi3QlzaNp8Dpv/6M79T1xDSKVtdLlwKt99+YvbY5YIJbljSaYFvp/RM8ntoZb2UEt7qKU9/KFlQGAAo+7qy5qVg9m/Fe68cSoVKm5i8eLr6dm3A+VqLGHEtZPZnrTD7VFd4w8dSyJ91/xMcHCw2yOIj6ilPdTSHmppD39rWb5yeV5+ezh7knqy/Mff6X3RZLIyKzP1g5HUaFSW+s1m89ITn5x1p/D4W8eSQgt8P5Oenu72COIjamkPtbSHWtrDn1u26RLJ19+OJHNPQ9556QuaNJ3LH4nduWfsVYRU2kbnLtP45tOf3R6zWPhzRzdpge9nKleu7PYI4iNqaQ+1tIda2sOGlgGBAYy+ux9rV13D3j893D5qKhUqbmTJkqH0GdSFMtWWMWTgJBLXbHZ71CJjQ0c3aIHvZ7Zu3er2COIjamkPtbSHWtrDtpYVq1bk1XeHsycp/yo8l10yiZycMD78bBQNIqtTs+FXPHz3DDIO2vWkVNs6Fhe90JWfyc3NJSgoyO0xxAfU0h5qaQ+1tMfZ0NKT5+Gjyd8y/u29rFp7ESbjHJzQPbRsuoB7R5Xn2pv6EBDo33u5Z0PHM6EXurLUmjVr3B5BfEQt7aGW9lBLe5wNLQMCAxhyYx9il19H+s4IHv33TGrU/JW4VQO5/tZLCau6jkv7TCZ2cbzbo3rtbOhYFLSDLyIiImKRzev+5NGxC5izuDkHd7QHJ49KtX7k6p5/Mu6Jyzin5jlujyhe0g6+pWJiYtweQXxELe2hlvZQS3uczS3rNanDzI9Hk7a9PQtmL6ZLl+mkptbjzSkjqFo/lEYtZjHhv7P84pKbZ3PHf0I7+CIiIiKWy83J5Y3xs3n9Q9iwvjccLktg2SQ6RP7AI3fVp/fALm6PKIWgHXxL6SdZe6ilPdTSHmppD7UsKCg4iDv/cxXrV1/FnqTD3DZyMhUq5L9qbkm+5KY6ekc7+CIiIiJnqZVLVvPoU8tZuOwCsvc2h8BsatRbyLDL9/HwEwMJKxPm9ohyHO3gWyo+3n+fCS8FqaU91NIeamkPtSyc6E4tmTtvFBm7mvLBO/OJjp7F9u1teHrC9URUPURUm/eZ+eY8PHkeV+ZTR+9oB9/PZGdnU7p0abfHEB9QS3uopT3U0h5q6b2Mgxk89/hnTP6yLFsTL4a8EEpXWkOPNr/xxMOtadMlsthmUceCtINvqeTkZLdHEB9RS3uopT3U0h5q6b2wMmE8/sJQtqzvyx/xu7juqncpVSqN+d+MpO2FLahY+wduvH4y25N2FPks6ugdLfD9TNWqVd0eQXxELe2hlvZQS3uopW/8/yU3O7Bg9mK6dp3KwYM1eXfmSGo0LEedxnN4/P73yDiYUSRfXx29owW+n0lNTXV7BPERtbSHWtpDLe2hlr7Xq39nfvhhJNkp5/H2S5/TrMUXbEnuyLjx1xFxbjpRbd5n2utf+fR8fXX0jhb4fiYkJMTtEcRH1NIeamkPtbSHWhadgMAAbry7P7/HDiFtRwj/uWs6NWr8RtyqAQy//TJCq2ykV88p/Pz18n/8tdTRO1rgi4iIiIhXIspF8NSEG9iy4QqS1u7mhsGTCA3fzaLvRnDhJW0pV2MpQ6+ezB9r/3R71LOKFvh+Jisry+0RxEfU0h5qaQ+1tIdaFr86jWoz7YNRpG7pwpIFy+l90SSyssrz3icjqd/yXGo0nMcDt08nbV9aoe9THb2jBb6fKV++vNsjiI+opT3U0h5qaQ+1dFfHXm35+ttRZO5uzHsT5xHd6hN27mjF86/fQPnqeTSN/JjXn5tNXu6pz9dXR+9oge9ndu3a5fYI4iNqaQ+1tIda2kMtS4aAwACuvflSYpcN5dDu8vz3ofepc94PrFt3Gbc/eCUhlf6kc5dpzP/kpxN+vjp6Ry905Wf0gg/2UEt7qKU91NIealmy7UzexbjHvmLW93XZu6UrmEDCqsbQ54I4Hn+kI83bNgbU8a/0QleW2rBhg9sjiI+opT3U0h5qaQ+1LNnOrV2ViVNHkvJnD2J/XkPfyyZhPIF8NmckLS5owDn1FnLH6GksW/rPr8RzNtIOvoiIiIiUCJ/PWMhzE7ezPL4beQdrQ6mD1G+wgJsG5jLmP/0pFVLK7RFdpR18S8XExLg9gviIWtpDLe2hlvZQS//U//pe/LLkBrL2VuelJz6mfoOvSEzsyQNPXk1o5b20bvc+M9+c79MX07KRdvBFREREpMTat2sfT46dw4cLq7IzqQfklSa4wgY6Ry7l4THn07NfB7dHLDbawbeUdiTsoZb2UEt7qKU91NIOMTExVKxakf9NHM6OTZeQuHo7118zifDwnXz/ww306t+BiHNjGHDFZFb/luD2uCWGdvBFRERExO8s+34lT4yP4bsVbcjcEwVOHhVr/syVFyYx9vGLqFW/htsj+px28C0VFxfn9gjiI2ppD7W0h1raQy3tcKqO7bpHM3feaDJ2RzHng+/o3Hk6aWm1mPTecGqfX4laDefx4J3vkbY/vRgnLhm0g+9ncnNzCQoKcnsM8QG1tIda2kMt7aGWdjjTjp48D5Nf+YJX3svg93W9MBlVcUqn0rDRIm4e5HDHg30JLuW/fy60g2+pTZs2uT2C+Iha2kMt7aGW9lBLO5xpx4DAAEbffSWrV1zHoV1l+O+DM6lXfxEb1l/MPWMHEFJxN20ueJ/33/7W6ivxaAffz6SnpxMREeH2GOIDamkPtbSHWtpDLe3gq44p23fz5Ni5fPRdNXYl9QRPKUpVWE/nqF95+O4m9LiinQ+mLXrawbdUSkqK2yOIj6ilPdTSHmppD7W0g686Vq5+Di+/M5KdiZewcdWfXHfVO4SG7+K776+nZ992RJy7gkH9pvD7CjteAVkLfD+j3Qh7qKU91NIeamkPtbRDUXRs0KIhMz++kdQtF/LLwhgu6f0OeSaQT+eMoEW7+lSu8wM33TCNbUm7fP61i4sW+H4mJyfH7RHER9TSHmppD7W0h1raoag7tu/Zlnlf30jmrmg+n/EtHTtN58CBWrwzYxg1G5SjdqOveHjMBxxM9a8r8WiB72c8HnufEHK2UUt7qKU91NIeammH4uzYb+jFLPl5BFkpdZg4/lOaR85i67Y2PP3yEMqdm0uTlrN4+anZ5BzOLbaZvKUFvp8JCwtzewTxEbW0h1raQy3toZZ2cKNjYFAQN987kPiYoaTvDOfx+6ZTp/4iEtb3ZswjVzL/k5+KfaYzpQW+n9m3b5/bI4iPqKU91NIeamkPtbSD2x3DykTw2PM3sHnNQHYlHuLhMVPpe10PV2cqDF0m089kZmYSGhrq9hjiA2ppD7W0h1raQy3toI4F6TKZltq8ebPbI4iPqKU91NIeamkPtbSDOnpHO/h+xuPxEBCgn8tsoJb2UEt7qKU91NIO6liQdvAttWrVKrdHEB9RS3uopT3U0h5qaQd19I528EVERERE/IB28C0VExPj9gjiI2ppD7W0h1raQy3toI7e0Q6+iIiIiIgf0A6+pWJjY90eQXxELe2hlvZQS3uopR3U0Tvawfczeja5PdTSHmppD7W0h1raQR0L0g6+pRISEtweQXxELe2hlvZQS3uopR3U0Tta4PuZevXquT2C+Iha2kMt7aGW9lBLO6ijd7TA9zPbt293ewTxEbW0h1raQy3toZZ2UEfvaIHvZypWrOj2COIjamkPtbSHWtpDLe2gjt7RAt/PZGRkuD2C+Iha2kMt7aGW9lBLO6ijd7TA9zN6Jrk91NIeamkPtbSHWtpBHb2j75qfCQ4OdnsE8RG1tIda2kMt7aGWdlBH71h5HXzHcfYAf7o9RxGpDKS4PYT4hFraQy3toZb2UEs7qGNBdYwxVU53IysX+DZzHGdFYV7gQEo+tbSHWtpDLe2hlnZQR+/oFB0REREREYtogS8iIiIiYhEt8P3P224PID6jlvZQS3uopT3U0g7q6AWdgy8iIiIiYhHt4IuIiIiIWEQL/BLKcZw+juOsdxxnk+M4D57g48Mdx9njOM6qI2+j3ZhTTs1xnMmO4+x2HOf3k3zccRznlSOdVzuO06q4Z5TCKUTLbo7jHDjumHysuGeU03Mcp5bjON87jrPOcZw1juPcdYLb6Lj0A4VsqePSDziOE+I4zjLHceKOtHz8BLcp7TjOR0eOy98cx6lb/JP6jyC3B5C/cxwnEHgduAjYCix3HGeOMWbtX276kTHm9mIfUM7EVOA1YPpJPn4J0PDI2wXAm0d+lZJnKqduCfCzMeby4hlHvJQL3GOMiXUcpwwQ4zjOgr/8/arj0j8UpiXouPQH2UAPY0y64zjBwGLHceYbY3497jajgP3GmAaO4wwGngOucWNYf6Ad/JKpHbDJGPOHMeYw8CHQz+WZxAvGmJ+Afae4ST9gusn3K1DecZxqxTOdnIlCtBQ/YIzZYYyJPfLfB4F1QI2/3EzHpR8oZEvxA0eOtfQjvw0+8vbXJ4n2A6Yd+e9ZQE/HcZxiGtHvaIFfMtUAthz3+62c+C+tgUf++XiW4zi1imc08bHCthb/0OHIPzHPdxynmdvDyKkd+Sf+aOC3v3xIx6WfOUVL0HHpFxzHCXQcZxWwG1hgjDnpcWmMyQUOAJWKd0r/oQV+yXSin0j/+pPsl0BdY0xLYCH//1Ot+JfCtBb/EEv+S4hHAq8Cn7s8j5yC4zgRwKfAGGNM2l8/fIJP0XFZQp2mpY5LP2GMyTPGRAE1gXaO4zT/y010XJ4BLfBLpq3A8TvyNYHtx9/AGLPXGJN95LfvAK2LaTbxrdO2Fv9gjEk7+k/Mxph5QLDjOJVdHktO4Mg5vp8C7xljPjvBTXRc+onTtdRx6X+MManAD0Cfv3zo2HHpOE4QUA6dNnlSWuCXTMuBho7j1HMcpxQwGJhz/A3+cj5oX/LPPRT/Mwe44chVO9oDB4wxO9weSs6c4zjnHj0f1HGcduT//brX3ankr440mgSsM8a8dJKb6bj0A4VpqePSPziOU8VxnPJH/jsU6AUk/OVmc4BhR/57EPCd0Ys5nZSuolMCGWNyHce5HfgGCAQmG2PWOI7zBLDCGDMHuNNxnL7kX0VgHzDctYHlpBzH+QDoBlR2HGcrMJb8Jw9hjJkIzAMuBTYBGcAIdyaV0ylEy0HAvxzHyQUygcH6n0+J1Am4Hog/cr4vwH+A2qDj0s8UpqWOS/9QDZh25CqCAcDHxpi5f1n3TAJmOI6zifx1z2D3xi359Eq2IiIiIiIW0Sk6IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYtogS8iIiIiYhEt8EVERERELKIFvoiIiIiIRbTAFxERERGxiBb4IiIiIiIW0QJfRERERMQiWuCLiIiIiFhEC3wREREREYsEuT1AUahcubKpW7eu22Oc0u9bYwkzwezf3YJq1aB69cJ9Xk5ODsHBwUU7nBQLtbSHWtpDLe2hlnZQx4JiYmJSjDFVTnc7Kxf4devWZcWKFW6PcUpRd4dSh/IsfHsF110H48e7PZGIiIiIlGSO4/xZmNvpFB2XhJtgDnGY0FDIyCj858XGxhbdUFKs1NIeamkPtbSHWtpBHb2jBb5Lwslf4IeFndkCPyoqquiGkmKllvZQS3uopT3U0g7q6B0t8F0S5pQig1zCwiAzs/Cfl5CQUHRDSbFSS3uopT3U0h5qaQd19I6V5+D7g/CA0hwilzJneIpOvXr1im4oKVZqaQ+1tIda2sMXLXNycti6dStZWVk+mEi8tW7dOrdHKHYhISHUrFnT6ycYa4HvkvDAUA7hoeoZnqKzfft26tevX3SDSbFRS3uopT3U0h6+aLl161bKlClD3bp1cRzHR5PJmcjKyiIkJMTtMYqVMYa9e/eydetWr39Q1Sk6LgkPCuVQkOeMT9GpWLFi0Q0lxUot7aGW9lBLe/iiZVZWFpUqVdLi3kVBQWffXrTjOFSqVOkf/cuRFvguCQsOIyMYQkPMGe3gZ5zJjaVEU0t7qKU91NIevmqpxb27PB6P2yO44p/+udMC3yXhweHkBUBIqZwzWuAHBCiZLdTSHmppD7W0h1rK2Ux/+l0SXjoCgOCgzDM6RUev5mYPtbSHWtpDLe1hc8sJEyYU6l8o6tatS0pKyt/eP27cOF544YWiGO20Jk6cyPTp0wEYPnw4s2bNAmD06NGsXbsWgIiIiGO3P9VO9vH3JQWdfSc2lRDhpctABgQFZJKRUa7Qn5eenk7lypWLcDIpLmppD7W0h1raw+aWEyZMYOjQoYSFhbk9yhm75ZZbTvj+d99994TvP9kpOrm5uSe9L9EOvmvCQsoAEBRw6IxO0bH1L6uzkVraQy3toZb2sKHloUOHuOyyy4iMjKR58+Z89NFHvPLKK2zfvp3u3bvTvXt3AP71r3/Rpk0bmjVrxtixYwvcx/jx42nXrh3t2rVj06ZNf/saiYmJ9OnTh9atW9OlS5cTXnf+0KFDjBw5krZt2xIdHc0XX3wBQGZmJoMHD6Zly5Zcc801XHDBBaxYsQIouAs/a9Yshg8fDpz8Xw+6det27HMB7rnnHlq1asUll1zCnj17jt3mP//5D127duXll18ucF/Hf35KSgp169YFYOrUqfTv358rrriCevXq8dprr/HSSy8RHR1N+/bt2bdv3+lD+CHt4LskPCx/1z7ASSczE4yBwjyfYuvWrTRu3LiIp5PioJb2UEt7qKU9fN5yzBhYtcp39wcQFQUTJpz0w19//TXVq1fnq6++AuDAgQOUK1eOl156ie+///7YDzFPPfUUFStWJC8vj549e7J69WpatmwJQNmyZVm2bBnTp09nzJgxzJ07t8DXuOmmm5g4cSINGzbkt99+49Zbb+W7774rcJunnnqKHj16MHnyZFJTU2nXrh29evXirbfeIiwsjNWrV7N69WpatWrlk2/LoUOHaNWqFS+++CKPPvoojz/+OK+99hoAqamp/Pjjj0D+DwuF8fvvv7Ny5UqysrJo0KABzz0+3VUHAAAgAElEQVT3HCtXruTuu+8+9n2xjXbwXRIeXgHIX+ADFPZKSA0aNCiqkaSYqaU91NIeamkPG1q2aNGChQsX8sADD/Dzzz9TrtyJT+n9+OOPadWqFdHR0axZs+bYuewAQ4YMOfbrL7/8UuDz0tPTWbp0KVdddRVRUVHcfPPN7Nix42/3/+233/Lss88SFRVFt27dyMrKIjk5mZ9++omhQ4cC0LJly2M/VPxTAQEBXHPNNUD+efqLFy8+9rGj7z8T3bt3p0yZMlSpUoVy5cpxxRVXAPnf36SkJJ/MXNJoB98l4eHlAQhw0oD8F7sKDT39561Zs4bIyMiiHE2KiVraQy3toZb28HnLU+y0F5VGjRoRExPDvHnzeOihh7j44ot57LHHCtxm8+bNvPDCCyxfvpwKFSowfPjwAtdPP/5Jqn99wqrH46F8+fKsOs2/TBhj+PTTTzn//PP/9rGTPQn2+Pf/k+u5Z2VlFbiv8PDwE94uKCjo2Pn6f/16pUuXPvbfAQEBx34fEBBAbm6u17OVZNrBd0lYRP4OvuEgUPgXu9L/eOyhlvZQS3uopT1saLl9+3bCwsIYOnQo9957L7GxsQCUKVOGgwfz1w9paWmEh4dTrlw5du3axfz58wvcx0cffXTs1w4dOhT4WNmyZalXrx6ffPIJkL+Qj4uL+9scvXv35tVXX8UYA8DKlSsBuPDCC3nvvfeA/NNgVq9efexzqlatyrp16/B4PMyePfuMHrfH4zl2dZ3Zs2fTuXPn035O3bp1iYmJATj2uWczLfBdEl6mEgAOBwAK/UTbo394xf+ppT3U0h5qaQ8bWsbHx9OuXTuioqJ46qmneOSRR4D88+YvueQSunfvTmRkJNHR0TRr1oyRI0fSqVOnAveRnZ3NBRdcwMsvv8z//ve/v32N9957j0mTJhEZGUmzZs2OPYH2eI8++ig5OTm0bNmS5s2b8+ijjwL5T+5NT0+nZcuWPP/887Rr1+7Y5zz77LNcfvnl9OjRg2rVqp3R4w4PD2fNmjW0bt2aBQsW/O1fLU7k3nvv5c0336Rjx44nvDTo2cY5+tOYTdq0aWOOfyZ2SbTjz9+pPrUFN/8xnrem30tsLERHuz2ViIiIHLVu3TqaNGni9hh+o1u3brzwwgu0adPG7VGscKI/f47jxBhjTvsN1g6+S8LLVQEgz9kPaAf/bKSW9lBLe6ilPdTSDocOHXJ7BL+kJ9m65OgpOkcX+Onphfu81q1bF9VIUszU0h5qaQ+1tIdaFr8ffvjB5/d5sifVyqlpB98lgYFBhOVA7pEFfmF/QI2Pjy/CqaQ4qaU91NIeamkPtbRDxpm8GqgcowW+iyJyAsgJPLMFfqNGjYpwIilOamkPtbSHWtpDLe0QEhLi9gh+SQt8F5XJDSArIP8lkgu7wE9OTi7CiaQ4qaU91NIeamkPtbTD4cOH3R7BL2mB76IITxDZgalA4Rf4VatWLcKJpDippT3U0h5qaQ+1tENwcLDbI/glLfBdFGGCyQw+s1N0UlNTi3AiKU5qaQ+1tIda2sOWloGBgURFRR17e/bZZ095+7lz5xIdHU1kZCRNmzblrbfeKqZJi4atrzRb1HQVHReVMaXYG5BJ6dKFX+DrXDR7qKU91NIeamkPW1qGhoayatWqQt02JyeHm266iWXLllGzZk2ys7NJSkoq2gGLWECA9qK9oe+aiyKc0qQH5BIeXvgFvoiIiMiiRYuIjo6mRYsWjBw5kuzsbA4ePEhubi6VKuVfirt06dKcf/75Lk8qbtAOvosiAkPOeIGflZVVtENJsVFLe6ilPdTSHr5uOebrMazaWbid9MKKOjeKCX0mnPI2mZmZREVFHfv9Qw89RL9+/Rg+fDiLFi2iUaNG3HDDDbz55puMGTOGvn37UqdOHXr27Mnll1/OkCFD/HoX3OPxuD2CX/Lf4hYoExjGwSAP4eGFf6Gr8uXLF+1QUmzU0h5qaQ+1tIctLY+eonP07ZprrmH9+vXUq1fv2KVAhw0bxk8//QTAu+++y6JFi2jXrh0vvPACI0eOdHP8fywoSHvR3tB3zUURweGkBxrCww2HDjmF+pxdu3ZRtmzZIp5MioNa2kMt7aGW9vB1y9PttBcnY8wpP96iRQtatGjB9ddfT7169Zg6dWrxDFYEcnJyCAwMdHsMv6MdfBdFlI4gNxBCQz2FPkWndu3aRTuUFBu1tIda2kMt7WFzy8aNG5OUlMSmTZsAmDFjBl27diU9PZ0ffvjh2O1WrVpFnTp1XJrSN0qVKuX2CH5JC3wXlSmdv7MQUiqr0Av8DRs2FOFEUpzU0h5qaQ+1tIctLY+eg3/07cEHHyQkJIQpU6Zw1VVX0aJFCwICArjlllswxvD8889z/vnnExUVxdixY/169x70vBhv6RQdF0WElIMsKBWcyaFD4YX6nBYtWhTxVFJc1NIeamkPtbSHLS3z8vJO+P6ePXuycuXKAu8rXbo08+bNK46xik1YWJjbI/gl7eC7KCIs/wlAwYEZhd7Bj4mJKcKJpDippT3U0h5qaQ+1tMMhXUfcK1rgu6hMRAUAggLTC73Ab926dRFOJMVJLe2hlvZQS3uopR3Cwwt3hoMUpAW+iyIi8l+IIjAgTTv4ZyG1tIda2kMt7aGWdtAOvne0wHdRRJn8BX6Ak0Z2NuTmnv5ztCNhD7W0h1raQy3toZZ20A6+d7TAd1GZclUAcJw0oHCvZhsXF1eUI0kxUkt7qKU91NIeammHjIwMt0fwS36xwHccp7zjOLMcx0lwHGed4zgd3J7JFyLKVwXAOKlA4Rb4zZo1K8qRpBippT3U0h5qaQ+1tENoaKjbI/glv1jgAy8DXxtjGgORwDqX5/GJiApnvsA/+qIW4v/U0h5qaQ+1tIda5r/QlbeXzUxNTeWNN9447e2SkpJo3rz5CT/WrVs3VqxYUeivOXz4cMLCwjh48OCx991+++04jkNKSkqh78cXjp+9bt26J/z6J3t/SVDiF/iO45QFLgQmARhjDhtjUt2dyjfCylbCMZDn7AcKt8CvWbNmEU8lxUUt7aGW9lBLe6hl8Szwfa1BgwZ88cUXAHg8Hn7++Wdq1KhR7HP4uxK/wAfOA/YAUxzHWek4zruO4/ztGReO49zkOM4Kx3FW7Nixg5SUFHbs2MG2bdvYv38/iYmJZGZmsnbtWjweD7GxscD/P8s+NjYWj8fD2rVryczMJDExkf3797Nt2zaO3l9SUhLp6ekkJCSQm5t77Py+o/dx9Nf4+Hiys7PZuHEjaWlpJCcns3v3bnbv3k1ycjJpaWls3LiRwzk5RByGvMB9QP4C/+h9xMXFkZubS0JCAunp6SQlJZGSkkJiYmKJfkzZ2dnEx8ef8D5O9phKeqeiekzH/2rLY7KxU2EeU2JionWPycZOhXlMKSkp1j0mGzsV5jFt3rz5Hz+mvLw8Dh8+TG5uLllZWXg8HjIzMzHGHLu6y/G/GmPIzMzE4/GQlZVFbm4uhw8f5vDhw+Tk5JCdnU1eXt6x+zh6fvlf7ysjIwOPx8P69etp3LgxI0aMoFmzZgwZMoR58+bRsWNHGjRowK+//kpGRgaHDh3ihhtuoG3btkRGRvLFF1+QmprKY489xkcffURUVBQzZ85k6dKldOjQgaioKDp06MDq1avxeDzExMTQrl07WrZsScuWLYmLi+PBBx8kMTGRqKgo7r77btLS0ujevTvR0dE0b96cWbNmHXtMubm5DB06lJYtW3LllVcemwnyX4kXYM6cOXTo0IHo6GgGDRrE3r17ycnJKfB9ycvLY/Dgwbz//vsAzJ8/n/bt2xMYGHjs+9K/f3+io6Np1qwZr7/+Ojk5OWzcuJEGDRqwc+dOMjIy6NKlC19++SXPPfcc48ePB/L/JaBHjx4cOnSIhQsXMmTIEDweD3PnzqV9+/ZER0czcOBA9u/fT3Z2NsYYsrKyMMZgjDlhJ2PMsdYvvPACzZo1o1mzZrz44ovHHtNzzz1H8+bNadq0KRMmTODQoUMkJSXRqFEjhg0bRvPmzY99P/Ly8sjOziYnJ+fYn7u/Hk+FdnTwkvoGtAFygQuO/P5l4MlTfU7r1q2Nv6h2X4C5fMhgA8Z8883pb79nz56iH0qKhVraQy3toZb28EXLtWvXHvvvu+4ypmtX377dddepv/7mzZtNYGCgWb16tcnLyzOtWrUyI0aMMB6Px3z++eemX79+xhhjHnroITNjxgxjjDH79+83DRs2NOnp6WbKlCnmtttuO3Z/Bw4cMDk5OcYYYxYsWGAGDBhgjDHm9ttvNzNnzjTGGJOdnW0yMjLM5s2bTbNmzY59bk5Ojjlw4IAxJv97W79+fePxeMzmzZsNYBYvXmyMMWbEiBFm/PjxxhhjunbtapYvX2727NljunTpYtLT040xxjz77LPm8ccf/9vjHTZsmPnkk0/MBRdcYPbt22dGjx5tFi5caOrUqXOs5969e40xxmRkZJhmzZqZlJQUY4wx77zzjhk4cKB5/vnnzU033WSMMeaXX34xgwYNMsYY07lzZ9O2bVtz+PBhM27cODNx4sRTznV0dmNMga9/vKPvX7FihWnevLlJT083Bw8eNE2bNjWxsbEnff+pvmfHO/7P31HAClOI9XPQ/7F37/FxlnX+/9/XPcccm6RJk6bpudBzkyYpbSktJzlLBdQVRDkoq+th1d2vK7j+UNddd9VlPaCrrOIKfL8IonIQEUSQQik9pjQtLaFN2jTNoWnTpDnOTGbmvn5/TJImbY53Zuae+fT9fDzmkclkcveavhr4zJ177hn/UwHb1AOo11pv7/v8dwDus3E9UZURdiJojP8QnWAwGOMVUbywpRxsKQdbyiGl5dy5c7F8+XIAkRcOX3nllVBKYfny5aitrQUAvPzyy/jDH/6ABx54AADg9/tRV1d3zrba29tx55134tChQ1BKDfwdrV27Ft/+9rdRX1+PW265BRdccME536u1xj//8z/jjTfegGEYaGhoQHNzMwBg5syZWLduHQDgYx/7GB588EF8+ctfHvjebdu24cCBAwP36e3txdq1I58v5ZZbbsGTTz6J7du348c//vGQrz344IN45plnAADHjh3DoUOHMHXqVNxzzz347W9/i4ceegh79uwBEDlVakVFBTo7O+HxeFBaWopdu3Zh8+bNePDBBye8rpG8+eabuPnmmwdO6XnLLbdg8+bN0FoPe/vGjRvH/DubrIQf8LXWx5VSx5RSC7XW7wG4EsAEfkeR2NK1EwFHZMDv6hr7/qZpxnhFFC9sKQdbysGWckS75Q9/GNXNjZvH4xm4bhjGwOeGYSDU9wY6Wmv8/ve/x8KFC4d87/bt24d8fv/99+Pyyy/HM888g9raWlx22WUAgI9+9KNYvXo1XnjhBVxzzTV4+OGHMW/evCHf+/jjj+PkyZOoqKiAy+XCnDlz4Pf7AQBKqSH3PftzrTWuuuoqPPHEE+N6zLfeeitKS0tx5513wjDOHE2+adMmvPLKK9i6dStSU1Nx2WWXDayhp6cH9fX1AICuri5kZGQMrPNXv/oVLr74YqxYsQKvvfYaampqsHjxYtTU1ExoXSPRfYfwjPd2YOy/s8lKhmPwAeDvATyulNoLoATAv9u8nqhJ1y74XeM/i05qamqMV0TxwpZysKUcbCnH+dTymmuuwY9//OOBgfLtt98GAGRkZAw5I017e/vAC1YfeeSRgdsPHz6MefPm4Qtf+AI2btyIvXv3Dvu906ZNg8vlwmuvvYajR48OfK2urg5bt24FADzxxBO45JJLhqxvzZo12LJly8CZjXp6enDw4MERH8+sWbPw7W9/G5/97GeHDPjt7e3Izs5GamoqqqqqsG3btoGv3Xvvvbj99tvxrW99C3/7t387cPuGDRvwwAMPYMOGDVi/fj0eeughlJSUQCk14XWNZMOGDXj22WcHXnvwzDPPYP369SPePp6/s8lKigFfa71Ha12utV6htb5Ja91m95qiJUN54Xe1AxjfgN/a2hrjFVG8sKUcbCkHW8pxPrW8//77EQwGsWLFCixbtgz3338/AODyyy/HgQMHUFJSgt/85jf4yle+gq9+9atYt24dwuHwwPf/5je/wbJly1BSUoKqqirccccdmDp1KtatW4dly5bhn/7pn3D77bdj165dKC8vx+OPP45FixYNfP/ixYvx6KOPYsWKFWhtbcVnPvOZIevLy8vDI488gttuuw0rVqzAmjVrUFVVNepj+vSnP4358+cP/JYCAK699lqEQiGsWLEC999/P9asWQMAeP3117Fz586BId/tduNXv/oVAGD9+vVoamrC2rVrkZ+fD6/XOzBkW1nXcEpLS3HXXXfhoosuwurVq3HPPfdg5cqVI94+nr+zyVKj/fogWZWXl+uJnHfVTrd+eQ7eDjXj4I98+Jd/Ab7+9dHv7/P5+KYPQrClHGwpB1vKEY2W7777LhYvXhylFZEVpmkO2YsvQW1tLd7//vfjnXfeGfV+w/37U0pVaK3Lx/ozZP2NJaF0Rwq6XL1wu8e3B//IkSOxXxTFBVvKwZZysKUcbClDIBCwewlJKeFfZCtdhjMNndpEevr4BvzBvxKj5MaWcrClHGwpB1vK4PV67V5C1M2ZM2fMvfeTxT34Nstwp6PLDaSl6XEN+P2nfqLkx5ZysKUcbClHtFpKPJQ5mfS/Gdj5ZrL/7jjg2yzTkwmtgBRvaFynySwtLY39oigu2FIOtpSDLeWIRkuv14tTp05xyLdR/znkzydaa5w6dWpSv73gITo2y0zJAgJAqjeAri7XmPevqKhAWVlZHFZGscaWcrClHGwpRzRaFhUVob6+HidPnozSqmiient74Xa77V5G3Hm9XhQVFVn+fg74NstMyQJOAx6PH52d6WPen//jkYMt5WBLOdhSjmi0dLlcmDt3bhRWQxRfPETHZplpOQAAt7Mbg95PYkS7d++O8YooXthSDraUgy3lYEsZ2NEaDvg2y8zIBQC4XF3jGvBLSkpivCKKF7aUgy3lYEs52FIGdrSGA77NpkyZBgBwOjvGNeBbeYc1SkxsKQdbysGWcrClDOxoDQd8m2VOyQcAGI72cQ34PBZQDraUgy3lYEs52FIGdrSGA77NMnMKAADKOI1AAAgGR79/Y2NjHFZF8cCWcrClHGwpB1vKwI7WcMC3WUbuDACAdrQCwJjnws/JyYn1kihO2FIOtpSDLeVgSxnY0RoO+DZzpqYjtRfQRmTAH+swnfP1Hd0kYks52FIOtpSDLWVgR2s44CeAzKBCyGgDMPaAbxhMJgVbysGWcrClHGwpAztaw7+1BJAZciLoOAVg7AHf5Rr73W4pObClHGwpB1vKwZYysKM1HPATQKbpQsA1vgG/a6yD9ClpsKUcbCkHW8rBljKwozUc8BNAJtzwuyKH6Iz17zg3NzcOK6J4YEs52FIOtpSDLWVgR2s44CeATOWFzzO+F9nW19fHYUUUD2wpB1vKwZZysKUM7GgNB/wEMMVIRY93fC+yXbBgQRxWRPHAlnKwpRxsKQdbysCO1nDATwCZrnR0pbYDGHvA379/fxxWRPHAlnKwpRxsKQdbysCO1jjtXgABme50dLh9MAyNzk416n2Li4vjtCqKNbaUgy3lYEs52FIGdrSGe/ATQKZnCkwHkJGux9yDX1FREZ9FUcyxpRxsKQdbysGWMrCjNRzwE0BmajYAIC01NOZZdMrKyuKwIooHtpSDLeVgSznYUgZ2tIYDfgLoH/BTUnq5B/88wpZysKUcbCkHW8rAjtZwwE8AmRmRc7x63b4xB3w+k5WDLeVgSznYUg62lIEdreGAnwD6B3y3u3vMAX/fvn1xWBHFA1vKwZZysKUcbCkDO1rDAT8BZGblAwBczq4xB/wLL7wwDiuieGBLOdhSDraUgy1lYEdrOOAngMyc6QAAh6NjzAG/rq4uDiuieGBLOdhSDraUgy1lYEdrOOAngCk5hQAAh7NjzLPo5Ofnx2FFFA9sKQdbysGWcrClDOxoDQf8BJAxNbIHXxltY+7BP336dBxWRPHAlnKwpRxsKQdbysCO1nDATwBudwq8QUA7TyMQAILBke/r9XrjtzCKKbaUgy3lYEs52FIGdrSGA36CmBI0EHa0AsCYe/GJiIiIiEbCAT9BZIWcCDrbAIw+4Pv9/jitiGKNLeVgSznYUg62lIEdreGAnyCyTTd6XWPvwc/KyorTiijW2FIOtpSDLeVgSxnY0RoO+AkiS3vhd58CAHR0jHy/5ubmOK2IYo0t5WBLOdhSDraUgR2t4YCfILKMFPi8kT347e0j32/WrFlxWhHFGlvKwZZysKUcbCkDO1rDAT9BZDnS0JUWOQZ/tD34Bw8ejNOKKNbYUg62lIMt5WBLGdjRGg74CSLLlYnO9MiAP9oe/OXLl8dpRRRrbCkHW8rBlnKwpQzsaA0H/ASR5Z2CUGpksh9twK+oqIjTiijW2FIOtpSDLeVgSxnY0Rqn3QugiKzUHMDshsOh0d6uRrxfWVlZHFdFscSWcrClHGwpB1vKwI7WcA9+gshOzwMUkJYW5B788wRbysGWcrClHGwpAztawwE/QWRlTgMApKUERh3w+UxWDraUgy3lYEs52FIGdrSGA36CyMqeDgDwenpGHfArKyvjtCKKNbaUgy3lYEs52FIGdrSGA36CyJo6AwDgdneOeprMpUuXxmlFFGtsKQdbysGWcrClDOxoDQf8BJGVWwQAcLnaR92DX11dHacVUayxpRxsKQdbysGWMrCjNRzwE8SUaZF3anO4To864BcVFcVpRRRrbCkHW8rBlnKwpQzsaA0H/AThSZ+ClCCgnK2jDvgtLS3xWxTFFFvKwZZysKUcbCkDO1rDAT+BZPca0K7IgK/18PdJT0+P76IoZthSDraUgy3lYEsZ2NEaDvgJJCvkRNjZilAI8PmGv08wGIzvoihm2FIOtpSDLeVgSxnY0RoO+Akky/Qg6I78KmqkM+mYphnHFVEssaUcbCkHW8rBljKwozUc8BNIlvKi19MKACMeh5+amhrHFVEssaUcbCkHW8rBljKwozUc8BNIlpEGn/cUgJEH/NbW1jiuiGKJLeVgSznYUg62lIEdreGAn0CyXBnoSWsDMPKAX1hYGMcVUSyxpRxsKQdbysGWMrCjNRzwE0iWJxNd6acBjDzgHzlyJI4rolhiSznYUg62lIMtZWBHazjgJ5DslGyYKZFX14404C9atCiOK6JYYks52FIOtpSDLWVgR2s44CeQrLRcwBOZ7Ec6i86ePXviuCKKJbaUgy3lYEs52FIGdrSGA34CycrIAzydAEbeg19aWhrHFVEssaUcbCkHW8rBljKwozUc8BNIzpQCwDCRmhIYccCvqKiI76IoZthSDraUgy3lYEsZ2NEaDvgJJCdnBgAgxesbccAvKyuL44oolthSDraUgy3lYEsZ2NEaDvgJZGrebACA19uJ06eHv8/u3bvjuCKKJbaUgy3lYEs52FIGdrSGA34CycmPDPguTzva2oa/T0lJSRxXRLHElnKwpRxsKQdbysCO1nDATyCp2fnwhACnu3XEAb+qqiq+i6KYYUs52FIOtpSDLWVgR2s44CcQZRjICRhQ7lMjDvhz586N76IoZthSDraUgy3lYEsZ2NEaDvgJZmrQBdPVMuKA39jYGN8FUcywpRxsKQdbysGWMrCjNRzwE0yO9iDkOYnOTiAYHObrOTnxXxTFBFvKwZZysKUcbCkDO1rDAT/BTFWp6PW2AMCwZ9Lp6emJ84ooVthSDraUgy3lYEsZ2NEaDvgJJseRgZ7UyIA/3GE6hsFkUrClHGwpB1vKwZYysKM1/FtLMFM9WejOOAVg+AHf5XLFeUUUK2wpB1vKwZZysKUM7GgNB/wEk5MyFaG0yGQ/3IDf1dUV5xVRrLClHGwpB1vKwZYysKM1HPATzNT0PCBl5AE/Nzc3ziuiWGFLOdhSDraUgy1lYEdrOOAnmJwpBYB35AG/vr4+ziuiWGFLOdhSDraUgy1lYEdrOOAnmKk5M0bdg79gwYI4r4hihS3lYEs52FIOtpSBHa3hgJ9gcnJnAs5eeNyBYQf8/fv3x39RFBNsKQdbysGWcrClDOxoDQf8BDM1P/KWzCkpXcMO+MXFxXFeEcUKW8rBlnKwpRxsKQM7WpM0A75SyqGUelsp9Ue71xJLOYXzAQAeT8ewA35FRUWcV0SxwpZysKUcbCkHW8rAjtYkzYAP4IsA3rV7EbHmTc1EahBwutvQ2nru18vKyuK/KIoJtpSDLeVgSznYUgZ2tCYpBnylVBGAGwA8bPda4iGn1wHDc4p78IVjSznYUg62lIMtZWBHa5JiwAfwQwBfAWDavZB4mBpyA+7hB3w+k5WDLeVgSznYUg62lIEdrUn4AV8p9X4AJ7TWoz6FU0p9Sim1Sym1q6mpCS0tLWhqakJDQwPa2tpQU1MDn8+HAwcOwDRN7N69G8CZZ4a7d++GaZo4cOAAfD4fampq0NbWhoaGBvRvr7a2Fl1dXaiqqkIoFEJlZeWQbfR/3LdvHwKBAA4dOoSOjg7U1dXhxIkTOHHiBOrq6tDR0YFDhw4hEAhg375952wjB14E3S1oa9OoqqpCV1cXamtr0dLSgm3btiXlYwKAyspKhEKhcx5Tsnaa7GN69dVXxT0miZ3G85i2bdsm7jFJ7DSex7Rv3z5xj0lip/E8pu3bt4t7TBI7jfWYBv9MSnlMk+k0XkprPe4720Ep9R8APg4gBMALIBPA01rrj430PeXl5XrXrl1xWmH0feT/zMarWz+BU1u/gd5ewOU687VAIACPx2Pf4ihq2FIOtpSDLeVgSxnYcSilVIXWunys+yX8Hnyt9Ve11kVa6zkAbgXw19GGewnyXFPQk3YKAHD69NCv1dXV2bAiigW2lIMt5WBLOdhSBna0JuEH/PPRNO9U+DIjA/7ZZ9LJz8+3YewnXs8AACAASURBVEUUC2wpB1vKwZZysKUM7GhNUg34WutNWuv3272OWMtLywNSWwAAp04N/drps3fpU9JiSznYUg62lIMtZWBHa5JqwD9f5GXNAFIik31Ly9Cveb1eG1ZEscCWcrClHGwpB1vKwI7WcMBPQHlTZw3swT97wCciIiIiGg0H/ASUVzBvxEN0/H6/DSuiWGBLOdhSDraUgy1lYEdrOOAnoLwZFwDubjidvefswc/KyrJnURR1bCkHW8rBlnKwpQzsaA0H/ASUU7gAhgZSUtrPGfCbm5vtWRRFHVvKwZZysKUcbCkDO1rDAT8BOVxuTPUruLyt5wz4s2bNsmdRFHVsKQdbysGWcrClDOxoDQf8BJUXdMPhPXnOMfgHDx60Z0EUdWwpB1vKwZZysKUM7GgNB/wElae90N4T5+zBX758uT0LoqhjSznYUg62lIMtZWBHazjgJ6g8IwOh1JPnDPgVFRX2LIiiji3lYEs52FIOtpSBHa1x2r0AGl6eKwv+tBMItALhMOBwRG4vKyuzd2EUNWwpB1vKwZZysKUM7GgN9+AnqGkpufBnnoTWQFvbmdv5TFYOtpSDLeVgSznYUgZ2tIYDfoLKy8gf9s2u+ExWDraUgy3lYEs52FIGdrSGA36CysuaMTDgDz4Ov7Ky0qYVUbSxpRxsKQdbysGWMrCjNRzwE1Re7mwgNbLrfvCAv3TpUptWRNHGlnKwpRxsKQdbysCO1nDAT1B5BfOG3YNfXV1t04oo2thSDraUgy3lYEsZ2NEaDvgJKn/GwmGPwS8qKrJpRRRtbCkHW8rBlnKwpQzsaA0H/AQ1dfo8OIweOJ2BIXvwW84+MT6NaV/zPtz17F249JFL8eWXv4zmrma7lwSALSVhSznYUg62lIEdreGAn6AMw4ECvwG3t23IgJ+enm7fopLQ7w78DuW/KMczVc+gN9yLH23/EYofKsa7J9+1e2lsKQhbysGWcrClDOxoDQf8BFYQ9MCRchInT565LRgM2regJLOjYQc+9vTHsKpwFQ5/4TC2fnIr3v702wCA6x6/Die6T9i6PraUgy3lYEs52FIGdrSGA34CK0A6kNqM5kFHlJimad+Ckog/5MftT9+O6RnT8eytz2Jq6lQAwLJpy/DCR19AU1cT/vHP/2jrGtlSDraUgy3lYEsZ2NEaDvgJrMCZhWB6E04M2tGcmppq34KSyPe3fh/VrdX4+ft/jtzU3CFfKyssw33r7sPj+x7Ha0des2mFbCkJW8rBlnKwpQzsaA0H/ARWkJIHf+ZxNDdraB25rbW11d5FJYF2fzu+t+V7+MDCD+Cq+VcNe5+vrv8qijKL8I1N34jz6s5gSznYUg62lIMtZWBHazjgJ7DpmYVAejP8foXOzshthYWF9i4qCfx050/RHmjH1y/9+oj38Tq9+KeL/wmb6zZj89HNcVzdGWwpB1vKwZZysKUM7GgNB/wEVjB1NpAWOT6n/zj8I0eO2LiixBcyQ/jxjh/j6vlXo3R66aj3vaf0HuSm5uIH234Qp9UNxZZysKUcbCkHW8rAjtZwwE9gBfnzgfTIZN8/4C9atMjGFSW+Fw+9iKauJnym/DNj3jfVlYq7iu/C8weft+Xc+GwpB1vKwZZysKUM7GgNB/wEVlC0CEiLDJ79L7Tds2ePjStKfA+//TAK0gtwwwU3jOv+95Teg5AZwiN7HontwobBlnKwpRxsKQdbysCO1nDAT2AFs5edswe/tHT0w07OZ8e7juOFgy/gruK74HK4xvU9C3MX4pJZl+D/7v2/MV7dudhSDraUgy3lYEsZ2NEaDvgJLC0rD+nOkwDMgQG/oqLC1jUlsqfffRphHcbHVnxsQt/3kaUfwf6T+1HVUhWjlQ2PLeVgSznYUg62lIEdreGAn+CmBw14UtoGBvyysjJ7F5TAfnfgd1icuxhLpy2d0PfdsviWge+PJ7aUgy3lYEs52FIGdrSGA36CKzBT4Ug5MTDg7969294FJagT3Sfw+tHX8eElH57w9xZmFGLdzHVxH/DZUg62lIMt5WBLGdjRGg74Ca7AyATSjg+8yLakpMTeBSWoZ6uehalNfGjJhyx9/4eWfAiVzZU4dOpQlFc2MraUgy3lYEs52FIGdrSGA36Cm+7JRW965N1sAaCqKr7HiSeLPx78I+ZmzcWyacssff/Ni24GALxw6IVoLmtUbCkHW8rBlnKwpQzsaA0H/ARXlF6IUGYzmo9HBvy5c+favKLEEwgF8Ncjf8V1C66DUsrSNmZnzcbCqQvx55o/R3l1I2NLOdhSDraUgy1lYEdrOOAnuBk5s4H0ZnR2GfD5gMbGRruXlHA2121Gd7Ab111w3aS2c+2Ca7GpdhN8QV+UVjY6tpSDLeVgSznYUgZ2tIYDfoIryr9g4M2umpuBnJwcm1eUeF489CLcDjcun3P5pLZzzfxr4A/5sbluc5RWNjq2lIMt5WBLOdhSBna0hgN+gpsxcymQ0QQAaGoCenp6bF5R4nmx+kVcOvtSpLnTJrWdS+dcCo/Dg5eqX4rSykbHlnKwpRxsKQdbysCO1nDAT3AzLigFMhoAAA0NgGEw2WB17XV4t+VdXLvg2klvK9WVig2zN8TtOHy2lIMt5WBLOdhSBna0hn9rCc47ZSpynJE9+I2NgMvlsnlFiWVT7SYAwPvmvS8q27ti7hU4cPIATnafjMr2RsOWcrClHGwpB1vKwI7WcMBPAkWOLhiOABobga6uLruXk1A21W5CTkqO5dNjnm3D7A0AEJfj8NlSDraUgy3lYEsZ2NEaDvhJYKbKgDOtCY2NQG5urt3LSSibajfh0tmXwlDR+adcXlgOr9OLN46+EZXtjYYt5WBLOdhSDraUgR2t4YCfBIpcuTAzGtDQANTX19u9nIRx9PRRHDl9BJfNuSxq23Q73FhbtDYuAz5bysGWcrClHGwpAztawwE/CcxIn47QlEY01IexYMECu5eTMPqPv4/mgA9EDtPZc3wP2v3tUd3u2dhSDraUgy3lYEsZ2NEaDvhJoChnLpDRiIZGjf3799u9nISx6Wh0j7/vt2H2BmhobDm2JarbPRtbysGWcrClHGwpAztawwE/CcwouADIaEBXlxPz5xfbvZyEEe3j7/utKVoDp+GM+WE6xcVsKQVbysGWcrClDOxoDQf8JFA0ezmQEXmr5pdffsfm1SSGY+3HUHu6FpfOvjTq2051pWJV4Sq8fvT1qG97sIqKiphun+KHLeVgSznYUgZ2tIYDfhIoWlA6MODn5ET3cJRk9daxtwAAl8y6JCbbv2TWJdjdtBuBUCAm2weAsrKymG2b4ost5WBLOdhSBna0hgN+EsjMLkCaNzLgb9ly2ObVJIa3jr2FVFcqVuSviMn2V89Yjd5wL/Yc3xOT7QPcKyEJW8rBlnKwpQzsaA0H/CQxx3kKAOB2z7N5JYnhrfq3sKpwFVyO2LzD3UUzLgIA7GjYEZPtA9wrIQlbysGWcrClDOxoDQf8JDHPZcBwdWLv3ha7l2K7nmAP9hzfg4tnXhyzP6MoswjT06dje8P2mP0Z+/bti9m2Kb7YUg62lIMtZWBHazjgJ4k5nnwgsx7d3dl2L8V2uxp3IWSGYjrgK6Wwumh1TAf8Cy+8MGbbpvhiSznYUg62lIEdreGAnyTmZM6CmVWHg+91270U2/W/wHZN0ZqY/jmrZ6xGdWs1Wn2tMdl+XV1dTLZL8ceWcrClHGwpAztaE9UBXyn1+UHXs6K57fPd7IKFwJQ6HG9y2r0U222t34qFUxciNzU3pn/O6hmrAcTuOPz8/PyYbJfijy3lYEs52FIGdrQm2nvwZw+6/tUob/u8Nmd2MTClDqfaUuH3270a+2it8daxt7B25tqY/1llhWVQUNheH5vDdE6fPh2T7VL8saUcbCkHW8rAjtZEe8A3lFLrlVIGgKlR3vZ5bc6iNcCUowCA+nqbF2Oj6tZqtPS04OKi2B1/3y/Tk4kleUuwozE2e/C9Xm9Mtkvxx5ZysKUcbCkDO1oT7QH/KwCKAfwCwHNR3vZ5LadwAbxpkePQjh61eTE26j/+PpYvsB1s9YzV2F6/HVrruPx5RERERJMV1QFfax3WWv9Ea/1JrfXz0dz2+U4ZBma6mwEA5/PrTbbWb8UUzxQszlsclz9vddFqnPKdwpHTR6K+bf/5fKyVMGwpB1vKwZYysKM1kx7wlVLXKaW2K6XeU0o9pZSK7alNzmML0jsBmOf1HvwdDTtw0YyLYKj4nAAqlm94lZXF16FLwZZysKUcbCkDO1oTjSnppwD+EcAaAD8H8IBS6rYobJfOMjctByq9CXVHz8/DRXxBH/Y27x0YuuNhSd4SuB1u7Dm+J+rbbm5ujvo2yR5sKQdbysGWMrCjNdEY8Ju11lu01m1a61cAXAPga1HYLp1lTtYc6KyjqDl0fv666u3jbyOsw3Ed8N0ON5bkLYnJgD9r1qyob5PswZZysKUcbCkDO1oTjQG/Vin1b0opd9/nQQCdUdgunWVuwWJgSh2O1AbtXoot+g+TWVW4Kq5/bnF+MSqbK6O+3YMHD0Z9m2QPtpSDLeVgSxnY0ZpoDPgawC0Ajiml3gRQDWCTUuqCKGybBlkwpzTyZlfNKTBNu1cTfzsadmBm5kxMz5ge1z+3pKAEx7uOo7krur8mXL58eVS3R/ZhSznYUg62lIEdrbE84Cul1iqllNb6Nq31EkTe5OpLAP4FQBqAh5VSx6K0TgIwf+klQNZRBEMunDhh92rir/8FtvFWnF8MAFHfi19RURHV7ZF92FIOtpSDLWVgR2smswf/TgAVSqknlVJ3AcjSWu/SWv9Sa/0FrfWlWuuZ0VkmAUBG7gxkeSPnyDwS/bM2JrRTPadQ01Zjz4Bf0DfgH4/ugF9WVhbV7ZF92FIOtpSDLWVgR2ssD/ha67/TWpcC+CaAbACPKKW2KqX+XSm1QSnliNYi6YzZ3gYAwOHDNi8kznY27gQAWwb8nJQczMycyT34NCK2lIMt5WBLGdjRmkkfg6+1rtJa/0BrfS2AKwC8CeDDALZPdtt0ruV5fgAmamrsXkl87WjYAQWFsun2PJMvLiiO+pl0uFdCDraUgy3lYEsZ2NGaqL7RFYBHAbRprf9ea10++eXR2Wa4M4HMBrz37vl1qswdDTuwJG8JMjwZtvz5xfnFqGqpgj8Uvb/3ysron5mH7MGWcrClHGwpAztaE4s3uvpPvtFV7JQsWA1k1+DA/i67lxI3Wmtsb9huy+E5/UoKShDWYRw4eSBq21y6dGnUtkX2Yks52FIOtpSBHa3hG10lmbS0mUBODWrr3GPfWYja07Vo6WmxdcDvP5NONA/Tqa6ujtq2yF5sKQdbysGWMrCjNXyjqyRTtv4DQHYNTrdnorvb7tXER/8bXNk54M/PmY80V1pUz6RTVFQUtW2RvdhSDraUgy1lYEdr+EZXSabXdCEzLXKOzPPlTDo7GnbA4/Bg+TT73uzCUAZW5K+I6pl0WlpaorYtshdbysGWcrClDOxoTTTOosM3uoqj9PR0zElvAoDz5kw6Oxp3oHR6KVwOl63rKM4vRmVzJbTWUdleenp6VLZD9mNLOdhSDraUgR2ticYefACA1trPN7qKvWAwiOKpPQDOjwE/ZIZQ0Vhh6+E5/YoLinHafxp17XVR2V4wGIzKdsh+bCkHW8rBljKwozVRG/ApPkzTxMrp+YC3Ffsq2+1eTsztP7EfvpAPq2estnspKCkoAYCoHaZjmmZUtkP2Y0s52FIOtpSBHa3hgJ9kUlNTsXh2OTD1IPZVyn8tcyK8wLbf8mnLoaCi9kLb1NTUqGyH7MeWcrClHGwpAztawwE/ybS2tmLJiiuBvHdRU5tm93JibkfDDuSk5GBe9jy7l4I0dxoW5CzAnubonCqztbU1Ktsh+7GlHGwpB1vKwI7WOO1eAE1MYWEhvO45cGc/j/aObLS3A1Om2L2q2NnRuAMXzbgISim7lwIgchz+201vR2VbhYWFUdkO2Y8t5WBLOdhSBna0JuH34CulZiqlXlNKvauU2q+U+qLda7LTkSNHoBwOzEyPnCrzvfdsXlAMdfd2450T7+CiQvsPz+lXkl+CmrYadAYmf3jUkSNHorAiSgRsKQdbysGWMrCjNQk/4AMIAfg/WuvFANYA+JxSaonNa7LNokWLAADLcyO/snr3XTtXE1u7m3bD1GZCHH/fr7gg8o62e5v3Tnpb/S0p+bGlHGwpB1vKwI7WJPyAr7Vu0lrv7rveCeBdADPsXZV99uyJHP9dPtcLGL2o3CX3TDr9L7BdNWOVzSs5ozg/MuBH40w6/S0p+bGlHGwpB1vKwI7WJPyAP5hSag6AlQC2D/O1TymldimldjU1NaGlpQVNTU1oaGhAW1sbampq4PP5cODAAZimid27dwMAKioqAAC7d++GaZo4cOAAfD4fampq0NbWhoaGBvRvr7a2Fl1dXaiqqkIoFEJlZeWQbfR/3LdvHwKBAA4dOoSOjg7U1dXhxIkTOHHiBOrq6tDR0YFDhw4hEAhg3759w26jsrISoVAIVVVV6OrqQm1tLVpaWjB9+nQ0NDRgQeESYOoh7NrZkvSPaaROW+u3YkbqDExLm5Ywj6n5UDNyUnLwyjuvWHpMgzv1v2GW3Y9psp0k/tub6GOaPn26uMcksdN4HlNpaam4xySx03geU1FRkbjHJLHTWI9p8M+klMc0mU7jpaL1rpyxppRKB/A6gG9rrZ8e7b7l5eV6165d8VlYnFVUVKCsrAw1+97Ago+cRMGptWhqlvkClJk/mIn1s9bj1x/8td1LGeKKR69Ad7Ab2+8553nmhPS3pOTHlnKwpRxsKQM7DqWUqtBal491v6TYg6+UcgH4PYDHxxrupev/Rz536Tq4s6twoiUPEt/krb6jHvUd9VhbtNbupZyjOL8Y+5r3IWyGJ7Ud/gdLDraUgy3lYEsZ2NGahB/wVeT8iL8E8K7W+vt2r8du/b9qMgwH5qQfgWm6RJ5JZ1v9NgDAmqI1Nq/kXCUFJfCFfKhurZ7UdvpbUvJjSznYUg62lIEdrUn4AR/AOgAfB3CFUmpP3+V6uxdll5KSkoHrpYUtAIA9e+S9jfO2+m3wODwDZ61JJP1r2nN8ci/8GdySkhtbysGWcrClDOxoTcIP+FrrN7XWSmu9Qmtd0nf5k93rsktVVdXA9UuXZQOOADa/Wm/jimJjW/02lBWWwe1w272UcyzOXQyn4Zz0mXQGt6TkxpZysKUcbCkDO1qT8AM+DTV37tyB6+UrLgPyDmDnrh77FhQDveFeVDRVYM2MxDs8BwA8Tg+W5C2Z9IA/uCUlN7aUgy3lYEsZ2NEaDvhJprGxceD60jUbgWl7cag218YVRd/e5r3wh/wJefx9v+L84kkfojO4JSU3tpSDLeVgSxnY0RoO+EkmJydn4HpKRjbys/ejqysXJ0/auKgo23psKwBg7czEO4NOv+L8YjR2NqKlp8XyNga3pOTGlnKwpRxsKQM7WsMBP8n09Aw9HGfptCYAwN69dqwmNrY1bMOMjBkoyiyyeykjKimIvOin8rj1w3TObknJiy3lYEs52FIGdrSGA36SMYyhyS5dqAAAb74q51dY2+q3JfThOUB0zqRzdktKXmwpB1vKwZYysKM1/FtLMi6Xa8jnV65aDaQ3YtPmEzatKLpOdJ/A4bbDCT/g56bmojCjcFIvtD27JSUvtpSDLeVgSxnY0RoO+Emmq6tryOel6/8GKNyFfVVZNq0ourbXbweQmG9wdbaSgpJJDfhnt6TkxZZysKUcbCkDO1rDAT/J5OYOPWNOSnYepk99G6da5qC93aZFRdHW+q1wGk6UTU/8t6Yuzi/GgZMHEAgFLH3/2S0pebGlHGwpB1vKwI7WcMBPMvX1576pVWlR5LYd28PxXk7UbTm2BSsLViLFlWL3UsZUnF+MkBnCuy3vWvr+4VpScmJLOdhSDraUgR2t4YCfZBYsWHDObdeWpwIA/vT0e/FeTlQFQgFsr9+O9bPW272UcZnsmXSGa0nJiS3lYEs52FIGdrSGA36S2b9//zm3XXn5tUDOQbzxls+GFUXPzsadCIQD2DB7g91LGZcFOQuQ4kyxfCad4VpScmJLOdhSDraUgR2t4YCfZIqLi8+5bWHZ1XBP34GqI4l73vjxeOPoGwCAS2ZdYvNKxsdhOLA8f7nlF9oO15KSE1vKwZZysKUM7GgNB/wkU1FRcc5thuHAhbnvoqcrH8l8qNrmus1YmrcUU1On2r2UcSvOL0ZlcyW01hP+3uFaUnJiSznYUg62lIEdreGAn2TKyoY/u8yViyPv9Pb874/GczlREzJD2FK3JWkOz+lXUlCCVl8r6jsm/sxqpJaUfNhSDraUgy1lYEdrOOAnmZGeyd72gZWAuwNPP3MsziuKjsrjlejs7UyaF9j2K86P/OrQymE63CshB1vKwZZysKUM7GgNB/wkM9Iz2bIr/gaOoi2oeKcgziuKjs11mwEA62cn14C/In8FAGtn0uFeCTnYUg62lIMtZWBHazjgJ5l9+/YNe7vT7cX86RVoO7UAJ0/GeVFR8MbRNzAvex6KMpPrhcIZngzMz56PPc0TP5POSC0p+bClHGwpB1vKwI7WcMBPMhdeeOGIX7ty2WkAwB9+UxOv5USFqU28cfSNpDs8p19xQbGlPfijtaTkwpZysKUcbCkDO1rDAT/J1NXVjfi1uz+0GnD24Mnf18ZvQVGw5/genPKdwvvmvc/upVhSnF+M6tZqdPd2T+j7RmtJyYUt5WBLOdhSBna0hgN+ksnPzx/xa+WX3wJP0Wbs2DsrjiuavL/U/AUAknbALykogYbGvhMT+zXiaC0pubClHGwpB1vKwI7WcMBPMqdPnx7xa8rhQPGcnehovQA11YE4rmpyXjnyCpZNW4aC9OR8gXD/mXQm+o62o7Wk5MKWcrClHGwpAztawwE/yXi93lG/fusVDgDAz3+4PR7LmTRf0IfNRzfjqnlX2b0Uy2ZNmYUsb9aEj8MfqyUlD7aUgy3lYEsZ2NEaDvjC3Pm3HwWm1OK5Vyb+zqp22HJsCwLhQFIP+EqpgXe0JSIiIrIbB/wk4/f7R/16TsFsFMz6Kw4dLkVvb5wWNQl/qfkLXIYr6d7B9mzF+cXY27wXpjbH/T1jtaTkwZZysKUcbCkDO1rDAT/JZGVljXmfG0oaYQYz8PgvJ35u9nh7+fDLuHjmxUhzp9m9lEkpLihGd7AbNa3jP0XpeFpScmBLOdhSDraUgR2t4YCfZJqbm8e8z1e+cDng7sRD/9sYhxVZd6z9GPYc34MbLrjB7qVMWklBCYCJvdB2PC0pObClHGwpB1vKwI7WcMBPMrNmjX0KzAvL1yFnzkvYvX8VQqE4LMqi5w8+DwC4ceGNNq9k8pbkLYHTcE7oOPzxtKTkwJZysKUcbCkDO1rDAT/JHDx4cFz3u2FVNUK+PDz1q7djvCLrnj/4PC7IuQALpy60eymT5nV6sTh38YT24I+3JSU+tpSDLeVgSxnY0RoO+Elm+fLl47rf1/7xSsDVjR/8/FiMV2RNZ6ATfz3yV2xcuBFKKbuXExUlBSV4+/j4n1CNtyUlPraUgy3lYEsZ2NEaDvhJpqKiYlz3W1h6EfLnvojd71yCnp5wjFc1cS/XvIzecC9uvDD5D8/pV1JQgsbORpzoPjGu+4+3JSU+tpSDLeVgSxnY0RoO+EmmrKxs3Pe96+oGmP4cfPfrf47hiqx57r3nkO3NxrpZ6+xeStT0v9B2vG94NZGWlNjYUg62lIMtZWBHazjgJ5mJPJP92rc+BpV5FA//LrFOQekL+vBs1bO4edHNcBpOu5cTNRM9kw73SsjBlnKwpRxsKQM7WsMBP8lM5JlsRvZUlC77ExqPrsfb28d/fvZYe+HQC+js7cRHl3/U7qVEVU5KDmZNmTXu4/C5V0IOtpSDLeVgSxnY0RoO+EmmsnL8p2EEgO/843xAaXzhy7titKKJ+/W+X6MgvQCXzbnM7qVEXUlBybj34E+0JSUutpSDLeVgSxnY0RoO+Elm6dKlE7r/+z54NQoWPI8tO6/BqebOGK1q/E77T+OFQy/g1qW3wmE47F5O1JXkl+C9U++hJ9gz5n0n2pISF1vKwZZysKUM7GgNB/wkU11dPeHvufeuduhAFr7w2edjsKKJ+f2B36M33Cvu8Jx+JQUlMLWJd068M+Z9rbSkxMSWcrClHGwpAztawwE/yRQVFU34e75438eRVvgWnvrLOnR1+GKwqvH7+e6fY1HuIpQXltu6jlhZOX0lgPG90NZKS0pMbCkHW8rBljKwozUc8JNMS0vLhL9HGQbuvf0QQp2z8am7fzvsfb75zUkubBx2N+3GjoYd+Ez5Z8S8udXZZk+ZjSmeKXi7aewX2lppSYmJLeVgSzmGbRmP/9lRVPFn0hoO+EkmPT3d0vd97T8+jowZm/Gbl65GS1PrOV//l3+Z7MrG9rOdP0OqKxV3FN8R+z/MJkqpyAttm8feg2+1JSUetpSDLeUYtmU8/mdHUcWfSWs44CeZYDBo6fsMh4F///tmmD0FuOW2P0Z5VWM71XMKv37n17ht2W3I8mbF/c+Pp5KCEuxt3ouwOfo7CFttSYmHLeVgSznYUgZ2tIYDfpIxTdPy937+3g9h9qKnsfnNj+DV596K4qrG9qPtP0JPsAf/sOYf4vrn2qGkoAQ9wR5Ut47+wqDJtKTEwpZysKUcbCkDO1rDAT/JpKamTur7n/3lAsDpx4f/PoRQMBSlVY2u3d+OB7c/iFsW34Kl0+Sf7mplQeSFtmO94dVkW1LiYEs52FIOtpSBHa3hgJ9kWlvPPX5+IkouXoHbb/wt2o5twIdu/r9RWtXofrjth2gPtONr678Wlz/PbovzFsNluMY8k85kW1LiYEs52FIOtpSBHa1x2r0AmpjCwsJJb+OxJz+BVy58Ec+99FE8wH8SiwAAIABJREFU9tMXcMdnb4jCyoZX31GP7731Pdyy+BaUTi+N2Z+TSNwON5ZOW4rdTbtHvV80WlL8aA2EQpFLOBy5mGbk4nIVork5cl3rM7effbHytdFun8jaJ/pYJ0opwDDG/3Ei943GtgwDcDiGXoY7mRd/LuVgSxnY0RoO+EnmyJEjWLJkyaS2YTgMbHrmAiy75DjuvrcES5fvBbAiOgs8y1f+8hWEzTAeuOqBmGw/UZVPL8fTVU9Daz3iKUGj0TKR9Q/Evb2xvwSDZ4bvsy/hcHS+NvphoCnx+mulaDNCgApDGSZghAFlQhkBKCNyW+R2E0qZA58PvWgow4RhmFAODaX6PnfoyG1G5KPh0H3XNQyHCWUgct3QMBwaDoeGwzHMExEn4DAUHA7A6QRczshHp1PB5QKcjshHl1PB6VJwORXcLgWnU8HtMuB2GXC5Bl3vu93jcsDtVvC4HZHrfbd53JHrXrdz4GtetxNetxMOw0iqUxxL/2/s+YIdreGAn2QWLVoUne2sWIAnf/QyPvx3a7H+g33nmK2sjMq2+/2haROeeOcJ3L/wU5hb1wHURXf7iazcLMDDvlbUbn0Rc9NmDHufRaYZ9b/zkWgN9PgNdPvOXHp8Cr6AAX+vAZ9fDfnoD/R/TcEfMCLXAwr+3r7b+77m8w++z1nfG4jNEYBKaXjcGm5X38Wp4XRquJwaTkf/BXAMuu50argdGqkOwOnVQ7/miHx//3XH4NtH+ZphaDj69x7DhMOhInuLlR7YY3zmuoYCxrhdw1Bnvg6l0R3uxulQO9pDHegMd6Lb7EZXuBvd4R50hbvQEepGV7gHnaEedIW60RXyoSfshz/ci7A+6zU2avy75V2GCy7DAZfq/+iEy+i79N3mUA4YMGBAwVAOKBhwwAGlI7cDfV/XTiiogY8KTigYMLQD6LuvggPQgNYGoAFTK0Ar6P6LqaABwDSggTO3D9wv8r1aA+j7qM/+fm1Am5GLaSqYpgFTA2ZYnfncVAib6sx9wpGPWp+5T//XIh+NYT4q6OCZP0trB0zTAExH3+cGtOmIrNt0RB6TjnwNpiPyd2BG/m5g9l20AegE+N+1CkeeEPVdlBH5XDnCUEYIMMIwjHDf55GPhhHue0IThsMRue5wmH1PaiLXHYaGw2nCaWg4zvo5dvZ97nL0PblxaLic6PvZR+TiAtxOBY9Twe0CvK7IdZdT481X6uF1O+BNccHrdWMjgM2v7IM3xY2UVC9S07xISfPCm5aCFK8LbreCwYOXE0q05p7zjdJWfheb4MrLy/WuXbvsXkZM7N69G6Wl0TvU5V/vewJff+AWIOzBYU825gZOR2W7TenAis8AMzuArQ8DntHPGClOxXSg/NPAU08BHz4w/u/TAPzwogOZ51w6kYFupKEbaehB6sD18Vx6kGbpcSiY8MIPL/xIgW9C173ww4MA3OiN6sWB5D6jggZwKhU4OgWomwIczQIaM4DmNKA5/czHk6lAyDH8NgwTyPJHLtn+M9czA0B6L5DWC6QFz3xMDQ5/W0oQcIfPXDxhwGECybOPVj4NIGwAIQPohYGA4YDfcKLXcMJvOBFQTvSqyMdA/+dG5LZe5UQvzlwPKicCyoWgciKIM7cFceZjCH0fVeR6/+dhnPk8DCdCOvIxrM/cZg66zdROhLQLpnbCHHTb4IvWTmhz6HWtnYAZuY7Blyg9wdFQ6Hu6ODIVBowg4AhCGYMvIRhGEEb/dUcIDiMIwwjBYYQHPjocYTiMEJyOMJyOMBwOE05HGC6HCafThMuh4XKacDoiOylcfU9UXE4FtxtwuyK/hfG4FTxuI/JbFY8Br9sBj9cBr9cFr9cZ+ZjiijxZSfHAm+pBSv8lzYvU9FSkpHpgGMn9Ex3tuSfZKaUqtNblY90vAXYJ0ERE+x/5/d+5DcGMP+Ff/7/rsShzB/70zztw5ezJvWK92/TjAzVfR7e/Do+vfACea86Pt5nWGvD1OtDW5QI6DDjf+S6euOQqdF1+NTp8LnT0OAd9dKKjxzXsx1B4fLuP3M4w0rxhpHlCAx9TPWFke8Io8oaQ5gkjzduFNE870gY+H/oxxR25eN0mvK6h171uE26nOexxyudyAkjvu1BYh1HbexJVgXq8F2jEwUAjantP4GjwJOp6W9CjA0Pu71Eu5DunIN+ZhRnOKSh1ZkU+d2Uh35mFac4pyHVkINuRjixHGtINb1IdKkHWKUR+upwAvDavZeKCfRfrtNYIw0SvGUIgrNETMuELavhCJnxhDV8I8Ac1/CENf1jDHwL8IY1ACAiEVeRj0ESgVyPQawIPA5/46EPoDWr0BoHekEIwBARDCsGwQjCkEAobCIYNhEIGQqYDobCBUNiBkOlAOOxE2HQiPOi6aToRCrlgmqkwTQdM0xV54mK6oPsu6P8YjlyH6YrOX/F4qBDgCAL9T1Qc5z5piXwM9T1JOfPR4ej/GIbDiDxhcRphOJ3mwEeXQ5954uLQcA367YrLHfnocSu43QY8LqPv0DADXo8DHo+z78mKE263E94UF1JSXJEnLCkepKS6kZWaiaaj9UhNT0FKWipcbg8Uf80yJu7BTzIVFRUoKyuL+naVAlRq5JXq//BvVfivf7jY0na6e7vxwac+iL8c/gue+cgz2LhwYzSXGTe9vcDJk8CJE5HLqVNAayvQ1jb049m3BQKjbzc1FcjMjFyczm4UFKQNfD7WJT0dSEs7c3Hy6bnttNZo7GzE7978HTrTO7Hn+B682/Iuqlur0RvuHbhftjcb87LnYXbWbMyeMhuzpszC7CmzBz7PScnhwJ4gYvXfWIq/YVsqZe1V5FGkw2GEe3vh6+5BT5cPvp4Aerr98PkCCPh64fMF4PeF4PcHIxdfEIFAGP7eMAKBMAK9YQR6TQQCZuRJSlAj0PeEJRjSkScrof4nLwZCIYWg2feEJexAMGwg3PeEJRR2nPVkxTHwpMXse5JiDjxZcUKHzzxp6X/igrArvoeQGcEhv2HBOb9lGfykJRh5wuIY+sTFec5vWsy+jxpOhwmXw4Sr/0lL3xMWl1MPHBL27X+/CdNnT4/fYx5kvHvwOeATgMh/817ddRQ3frAHPUcXo+jiN/Hrh4qwfvmccW/j6Omj+NBvP4TdTbvx8I0P4+6Vd8duwROkdWRIb2qKDOyDh/fhrre3j7yt9HQgJydyyc4e+nHw9Uff+yE2NT+Nyi+9jqwshYwMwBXHnTYUfW2+Nmyt34otdVuws3En9hzfg5M9Jwe+Pj97PpZOW4pFUxdhYe5CLJy6EAtzFyI3NdfGVRPRgAQY8CXSpoa/pxc9XT3w9fjg6/bD39MLX08AvoEnLYOerPiD8PtDkesBE4HecOS3Kr0meoNm5AlLb+TJSuSJS/8TFnXmtyvh/usOhMzIk5dw/xOWs37bYppOmGHXkCcuuu/Jy5DfsoRd4zok7K2/VGDt++zZEcBDdISK5bFoV5TNxsmqIG787Ov462NrsKFUY8n1L+Hf7svHTWtKRtzD2BHowM92/gz/tvnfAADPfuRZ3Ljwxpis8Wz9g3tjY2R4b2wcen3wx+He7dowgLy8yGXaNKCs7Mz1/o/TpgFTp54Z3sc7pLfvzsAfn98MM6sGOTkLzvk6jytMfE2dTXjl8Ct4s+5NbDm2BftP7gcAOJQDK/JX4MYLb0RJQQlSO1Lx4Q0fRqYn0+YV02Tx51IOtowfZSikpHuQku4BkB3VbdvR0TQjZ0+LPGnxwdftg6+nF76+37SUrE38N+3kHvwkY5omjBgce3b2To2d+0/g4188gvf+GnmS6Fm0CaVXHMEV13RjXlEGFBQaOhuws3EnXjn8CnqCPbhuwXX46Q0/xZysOZNeTzgc2Zve1DTyZbTBPTsbKCwEpk+PfOy/Pn36maE9Ly8ytMfqUL7K45Uo+Z8SPPHBJ3DrslvP+XqsWpJ1veFebKnbgj/X/BkvVb+EyubIWY6meKZg7cy1WDdzHdbNXIeLZlyENPeZFy6zpRxsKcewLbkHP+nwZ3IoHqIjdMA/cOBATM4HO9J/8/Yf6sS93zmM156bgZ5TuQBMILcKmLEDmP42phX14LLiOfjE+utx6cKV8HiGf/OYUChy2MvgS1vb8IP78eMYeNOgs2Vnnzu0n329oABISYDTkgfDQWR+JxOfW/U5PHD1ue8DEKuWNDFdvV3406E/4bcHfouXql9CV28XXIYL62atw7Xzr8U1C67BivwVMNTI/4NhSznYUo5hW3LATzr8mRyKA77QAd/n8yElBtPrWP/N0xrYswd4+jk/3tqmsWeXG62nzj2Hn8sVeQGo1mfe6TMcHv3Fp4YR2aPev4f97EtBwZmP3iQ7jcSah9fA6/Ri012bzvlarFrS2AKhAP7w3h/w5P4n8eKhF+EL+ZCflo+bFt2E6y+4HpfPuRwZnoxxb48t5WBLOYZtyQE/6fBncigegy9UY2Mj5s+fH/c/Vylg5Upg5crIhK010NICHDkSubS0AB0dkUt399C3hjeMyNA/ZcrQS3Z2ZGifNi1yP4nKC8vxaOWjMLV5zh5gu1qez95uehu/2vMrPL7vcbT6WjE9fTo+ufKT+PDSD2PdzHVwGNb+IbKlHGwpB1vKwI7WcMBPMjk5OXYvAUBk4O9/cepFF9m9msRVXliO/9753zh46iAW5Q59N75EaSldIBTAU/ufwo+2/wgVTRXwODy4efHNuLvkblw590rLQ/1gbCkHW8rBljKwozUc8JNMT08PsrOj+wp1ip3ywshv0SoaK84Z8Nkytk50n8BDux7CT3f+FM3dzVicuxg/ue4n+OjyjyI7Jbp/72wpB1vKwZYysKM1HPCTTKxeSf6Nb8Rks+e9RbmLkOpKxY6GHbh9xe1DvsazAsTG8a7j+M8t/4mf7foZfCEfrr/genxp9Zfwvnnvi9mbSbGlHGwpx7At+T+7pMOfSWs44CcZV4zeKemb34zJZs97TsOJVYWrsK1h2zlfi1XL89XxruP4zpvfwf9U/A+C4SBuX3E7vnrJV8/5zUkssKUcbCnHsC35P7ukw59Ja/i0KMl0dXXZvQSaoLVFa/F209vwh/xDbmfL6Oju7ca3Xv8WFjy4AD/Z8RPctuw2VH2+Co/e9GhchnuALSVhSznYUgZ2tIZ78JNMbi7f8j7ZrClag6AZREVjBdbNWjdwO1tOjqlNPFb5GL7216+hsbMRH1z8QXznfd/BgmHeNTjW2FIOtpSDLWVgR2u4Bz/J1NfX270EmqC1M9cCALbWbx1yO1tat695H9b97zrc/dzdmJk5E2/e/SZ+9ze/s2W4B9hSEraUgy1lYEdruAc/ySxYYM8AQ9ZNS5uGednzzhnw2XLi+g/H+a+t/4XslGw8etOj+PiKj8fsxbPjxZZysKUcbCkDO1rDPfhJZv/+/XYvgSxYW7QWW49txeB3jmbLidl8dDOW/2w5vvfW93Bn8Z2o+lwV7ii+w/bhHmBLSdhSDraUgR2t4YCfZIqLi+1eAlmwtmgtmrqacKzj2MBtbDk+gVAA971yHy595FIopbDpzk345Qd+iampU+1e2gC2lIMt5WBLGdjRGg74SaaiosLuJZAFa4rWAAC2HjtzmA5bju3AyQNY/fBqfHfLd3FP6T3Y8+k9uHTOpXYv6xxsKQdbysGWMrCjNWrwIQNSlJeX6127dtm9DKIBwXAQU74zBZ8q+xR+eO0P7V5OUvh/e/8fPv3HTyPNlYZfbvwlblx4o91LIiIispVSqkJrXT7W/bgHP8nwmWxycjlcWDVj1ZAX2rLl8PwhP/7uj3+Hjz/zcZQXlqPy7yoTfrhnSznYUg62lIEdreEefKI4ue+V+/D9rd9H+33tSHGl2L2chFTfUY+bnrwJFU0V+MrFX8G3r/w2nAZP9kVERARwD75Y+/bts3sJZNH6WesRNIPYVr8NAFuebWfDTqz6xSocPHUQz3zkGXz3qu8mzXDPlnKwpRxsKQM7WsMBP8lceOGFdi+BLFo3ax0UFN44+gYAthzsqf1PYcMjG+BxePDWJ9/CTYtusntJE8KWcrClHGwpAztawwE/ydTV1dm9BLIoy5uFkoISvFEXGfDZEtBa47tvfhcf+d1HUDq9FDv+dgeWTVtm97ImjC3lYEs52FIGdrSGA36Syc/Pt3sJNAkbZm/A1mNb0RvuPe9bmtrEl1/+Mu579T7cuuxWvHrHq5iWNs3uZVlyvreUhC3lYEsZ2NEaDvhJ5vTp03YvgSZhw+wN8IV82NW467xuGTJD+MRzn8D3t30fn1/1eTx+y+PwOr12L8uy87mlNGwpB1vKwI7WcMBPMl5v8g5BFHmhLQC8cfSN87alL+jDB5/6IB6tfBTfvPSbePC6B2Go5P5P0fnaUiK2lIMtZWBHa5Li/6pKqWuVUu8ppaqVUvfZvR4iq/LS8rAkbwleP/q63UuxRU+wBzf8+gY8/97z+Ml1P8E3LvsGlFJ2L4uIiEiUhB/wlVIOAP8N4DoASwDcppRaYu+q7OP3++1eAk3ShlkbsKVuC7p6uuxeSlz1BHtw4xM34vWjr+Oxmx/D5y76nN1Lihr+XMrBlnKwpQzsaE3CD/gALgJQrbU+rLXuBfAkgA/YvCbbZGVl2b0EmqQr5l6Bzt5O1Phr7F5K3PQEe7DxiY147chrePSmR/GxFR+ze0lRxZ9LOdhSDraUgR2tSYYBfwaAY4M+r++7bQil1KeUUruUUruamprQ0tKCpqYmNDQ0oK2tDTU1NfD5fDhw4ABM08Tu3bsBnHkL5N27d8M0TRw4cAA+nw81NTVoa2tDQ0MD+rdXW1uLrq4uVFVVIRQKobKycsg2+j/u27cPgUAAhw4dQkdHB+rq6nDixAmcOHECdXV16OjowKFDhxAIBAbewOHsbVRWViIUCqGqqgpdXV2ora1FS0sLqqurxT0miZ1Ge0xTO6dCQeHX234t5jGN1qm1oxVX/+/V+OuRv+IHl/4AV+ZdmfSP6exO1dXV4h6ThH97Vh5Tc3OzuMcksdN4HlNNTY24xySx01iPafDPpJTHNJlO46W01uO+sx2UUh8GcI3W+p6+zz8O4CKt9d+P9D3l5eV6165d8VpiXAUCAXg8HruXQZO06her4DJceOuTb9m9lJjqDfdi4xMb8XLNy/jVB36FO0vutHtJMcGfSznYUg62lIEdh1JKVWity8e6XzLswa8HMHPQ50UAGm1ai+0OHjxo9xIoCq6adxV2NOxAR6DD7qXETNgM445n7sCfa/6MX9z4C7HDPcCfS0nYUg62lIEdrUmGAX8ngAuUUnOVUm4AtwL4g81rss3y5cvtXgJFwdXzr0ZYh7GpdpPdS4kJrTU+/6fP4zf7f4P/vOo/8cnST9q9pJjiz6UcbCkHW8rAjtYk/ICvtQ4B+DyAPwN4F8BTWuv99q7KPv3HbFFyW1u0Fl6HF3+p+YvdS4mJr7/2dTxU8RDuXXcvvnzxl+1eTszx51IOtpSDLWVgR2sS/hh8KyQfg09yXP/49TjcdhhVn6+yeylR9eD2B/HFl76Ie1beg5/f+HOe556IiChKJB2DT4Pwmawci92L8d6p93C47bDdS4ma3x/4Pb700pdwy+Jb8ND7Hzpvhnv+XMrBlnKwpQzsaA334BPZpKa1Bgt+vAA/vOaH+OKaL9q9nEnbemwrrnjsCqwsWIlX73gVKa4Uu5dEREQkCvfgC9V/LlZKfl3HurAkbwn+cDD5XzNe3VqNjU9uxIyMGXju1ufOu+GeP5dysKUcbCkDO1rDAT/JLF261O4lUJQsXboUGy/ciNdrX0ebr83u5VjW0tOC6x+/HlprvHj7i8hLy7N7SXHHn0s52FIOtpSBHa3hgJ9kqqur7V4CRUl1dTU2LtyIsA7jpeqX7F6OJf6QHzc9eRPq2uvw3K3P4YKpF9i9JFvw51IOtpSDLWVgR2s44CeZoqIiu5dAUVJUVISLZlyEaWnTkvIwHVObuPu5u7Hl2BY8dvNjWDdrnd1Lsg1/LuVgSznYUgZ2tIYDfpJpaWmxewkUJS0tLXAYDrz/gvfjxUMvojfca/eSJuSbm76JJ995Ev9x5X/gb5b+jd3LsRV/LuVgSznYUgZ2tIYDfpJJT0+3ewkUJf0tb1p0E9oD7Xj18Ks2r2j8Hqt8DP/6xr/ikys/iXvX3Wv3cmzHn0s52FIOtpSBHa3hgJ9kgsGg3UugKOlvefX8qzHFMwVP7n/S5hWNzxtH38A9f7gHV8y9Aj+94afnzbnuR8OfSznYUg62/P/bu/cgq+vzjuOfZ1lEiUQkqBERBbU2Kmp06yWJ0XoZtURpjI1gYtBqEs1k2s4kM0mrtdHEZKyT1NhGba1GTcWaKAYvsUYSI1SKye6WdQNuBeQil0JW7gFWDufpHxzT3c1ezv78sd/ze3i/ZhhZ9sfuc3j7hWd/7B5ioGM2LPgFUy6XU4+AnLzTclj9MF32gcv05GtPakdpR+Kp+rborUX6+GMf11GjjtLjf/a49hmyT+qRagLnMg5axkHLGOiYDQt+wQwfPjz1CMhJ55ZTTpiiLW9v0XOLnks4Ud/e2vaWJk2fpDqr0zNTn9GB+x2YeqSawbmMg5Zx0DIGOmbDgl8w69evTz0CctK55bnjz9VBww/S9F9PTzhR7zpKHbrsh5dp+abl+vEVP9ZRo45KPVJN4VzGQcs4aBkDHbNhwS+YMWPGpB4BOencsr6uXldOvFIz22aqfVttPWOAu+tzz3xOs5fP1oOTH9yrnw6zN5zLOGgZBy1joGM2LPgFs3Tp0tQjICfdW177wWu1s7xTj7z6SKKJenbbnNv0cMvDuuWcWzR14tTU49QkzmUctIyDljHQMRtz99Qz5K6hocEbGxtTj7FHlMtl1dXxcVkEPbU87b7TtKO0Qy3Xt9TEs9M82vqorpxxpa468So99KcP1cRMtYhzGQct46BlDHTsysya3L2hv+v4FSuY+fPnpx4BOemp5bUfvFat61r1y1W/TDBRV3PfnKtrZl6js8adpfsuuY/lvg+cyzhoGQctY6BjNtzBB2rIlo4tGvsPYzXpmEma/ol0X3D7xoY3dPq/nq6R+47UvGvn6X3D35dsFgAAsBt38INqampKPQJy0lPLEcNG6LOnfFY/WvgjvbnpzQRTSRt3bNSk6ZO0q7xLz175LMt9FTiXcdAyDlrGQMdsuIMP1JjlG5drwl0T9OUzv6zbL7h9UN/3zl07dfEjF2v28tl64aoXdPaRZw/q+wcAAL3jDn5Qzc3NqUdATnprecTII3T5cZfr3qZ7tX774D3/r7vrhmdv0M+W/kz3XXIfy/0AcC7joGUctIyBjtmw4BfMySefnHoE5KSvljeddZO2dGzRt+d+e9DmuWPuHbr/v+/XjWfdqGknTxu09xsB5zIOWsZByxjomA0LfsG0tbWlHgE56avlxEMm6ooTrtB3X/mu1v123R6fZcZrM/SVWV/RFcdfoVv/+NY9/v6i4VzGQcs4aBkDHbNhwS+Y8ePHpx4BOemv5dfO/pq2l7br1pf27ML98oqX9ekZn9YZY8/Q9yd/X3XGbwsDxbmMg5Zx0DIGOmbDn+QFs3r16tQjICf9tTx29LH6QsMXdE/jPWpcvWe+aHzum3N10SMXaex7x2rmlJnab+h+e+T9RMe5jIOWcdAyBjpmw4JfMKNGjUo9AnJSTctvnPsNHfyeg3X9M9drV3lXru9/7ptzdeG/XahD9z9Uv7j6Fzr4PQfn+vb3JpzLOGgZBy1joGM2LPgFs23bttQjICfVtDxg3wN054V3qmlNk74+++u5ve/nFz/fZbkfM2JMbm97b8S5jIOWcdAyBjpmw4JfMHV1JIui2pafPP6TmnbSNN360q366ZKfvuv3e3/z/Zo0fZKOHnW0Xrr6JZb7HHAu46BlHLSMgY7Z8KtWMEOHDk09AnJSbUsz092T7tbxBx+vqU9M1YJ1CzK9v1K5pK/O+qque/o6nT/hfM2+erYOHXFopreFrjiXcdAyDlrGQMdsWPALZuvWralHQE4G0nL40OGaOWWmhg0Zpgt+cIFeXfvqgN7Xso3LdM6D5+j2l2/X50/9vJ6e+rRGDBsx0JHRC85lHLSMg5Yx0DEbFvyCGT16dOoRkJOBtpxw4ATN+sws1VmdPvLARzS9dbrcvc+fs33ndn1zzjd13PeOU8vaFk2/bLru/di9GjqEOyJ54lzGQcs4aBkDHbNhwS+YlStXph4BOcnS8riDjtO86+Zp4iET9akZn9K5D5+rZ19/Vm/vevt315S9rPn/O183v3izxt05Tjf+/EZN+oNJWvCFBZo6cWqeDwEVnMs4aBkHLWOgYzbW3x3AImpoaPDGxj3zvOGplUol1dfXpx4DOXg3LUvlku751T26bc5tWvvbtdpnyD6acOAE1dfVa+Xmldq4Y6NMpkuOvURfOvNL+ugRH815enTGuYyDlnHQMgY6dmVmTe7e0O91LPjF0tLSopNOOin1GMhBHi3f3vW2nl/8vOasmKM3NryhUrmk9+//fn3o8A/pvPHn6bD3HpbTtOgL5zIOWsZByxjo2BULftAFHwAAAHunahd8Pge/YJqamlKPgJzQMg5axkHLOGgZAx2z4Q4+AAAAUADcwQ+Kj2TjoGUctIyDlnHQMgY6ZsMdfAAAAKAAuIMfVGtra+oRkBNaxkHLOGgZBy1joGM23MEvmI6ODg0bNiz1GMgBLeOgZRy0jIOWMdCxK+7gB7VixYrUIyAntIyDlnHQMg5axkDHbFjwC+aQQw5JPQJyQss4aBkHLeOgZQx0zIYFv2A2btyYegTkhJZx0DIOWsZByxjomA0LfsHsu+++qUdATmgZBy3joGUctIyBjtmw4AMAAACBsOAXzI4dO1KPgJzQMg5axkHLOGgZAx2zYcEvmJEjR6YeATmhZRy0jIOWcdAyBjpmw4JfMGvXrk09AnJCyzhoGQct46BlDHTMhgW/YMaNG5euwjArAAAJ2klEQVR6BOSElnHQMg5axkHLGOiYDQt+wbz++uupR0BOaBkHLeOgZRy0jIGO2Zi7p54hdw0NDd7Y2Jh6DAAAACA3Ztbk7g39Xccd/IJpampKPQJyQss4aBkHLeOgZQx0zIY7+AAAAEABcAc/KD6SjYOWcdAyDlrGQcsY6JgNd/ABAACAAuAOflAtLS2pR0BOaBkHLeOgZRy0jIGO2XAHv2BKpZLq6+tTj4Ec0DIOWsZByzhoGQMdu+IOflCLFy9OPQJyQss4aBkHLeOgZQx0zIYFv2DGjh2begTkhJZx0DIOWsZByxjomA0LfsG0t7enHgE5oWUctIyDlnHQMgY6ZsOCXzD7779/6hGQE1rGQcs4aBkHLWOgYzYs+AWzc+fO1CMgJ7SMg5Zx0DIOWsZAx2xY8AumXC6nHgE5oWUctIyDlnHQMgY6ZsOCXzDDhw9PPQJyQss4aBkHLeOgZQx0zIYFv2DWr1+fegTkhJZx0DIOWsZByxjomA0LfsGMGTMm9QjICS3joGUctIyDljHQMRsW/IJZunRp6hGQE1rGQcs4aBkHLWOgYzbm7qlnyF1DQ4M3NjamHmOPKJfLqqvj47IIaBkHLeOgZRy0jIGOXZlZk7s39Hcdv2IFM3/+/NQjICe0jIOWcdAyDlrGQMdsuIMPAAAAFAB38INqampKPQJyQss4aBkHLeOgZQx0zIY7+AAAAEABcAc/qObm5tQjICe0jIOWcdAyDlrGQMdsuINfMHw1eRy0jIOWcdAyDlrGQMeuuIMfVFtbW+oRkBNaxkHLOGgZBy1joGM2Nb3gm9kdZtZmZq+a2ZNmNjL1TKmNHz8+9QjICS3joGUctIyDljHQMZuaXvAlvSDpBHc/UdLrkv468TzJrV69OvUIyAkt46BlHLSMg5Yx0DGbml7w3f2n7l6qvDhP0tiU89SCUaNGpR4BOaFlHLSMg5Zx0DIGOmZT0wt+N38u6bnUQ6S2bdu21CMgJ7SMg5Zx0DIOWsZAx2ySL/hmNsvMft3Dt8mdrrlRUknSI328nc+ZWaOZNa5Zs0bt7e1as2aNVq1apQ0bNmjJkiXavn27Fi5cqHK5/LunXXrnH1Bobm5WuVzWwoULtX37di1ZskQbNmzQqlWr9M7bW7ZsmbZu3aq2tjaVSiW1tLR0eRvv/Le1tVUdHR1atGiRNm/erBUrVmjdunVat26dVqxYoc2bN2vRokXq6OhQa2trj2+jpaVFpVJJbW1t2rp1q5YtW6b29nZt2rQp3GOK2Kmax7R06dJwjylip2oe06ZNm8I9poidqnlMdXV14R5TxE7VPKYtW7aEe0wRO/X3mDqfySiP6d10qlbNP02mmU2TdL2k89y9qg/jIj9NZnt7u0aPHp16DOSAlnHQMg5axkHLGOjYVbVPk1nTC76ZXSTpO5LOdvffDODn/UbS8j02WFqjJbWnHgK5oGUctIyDlnHQMgY6dnWEux/U30W1vuAvljRM0luVH5rn7tcnHCk5M2us5iM31D5axkHLOGgZBy1joGM29akH6Iu7H516BgAAAKBIkn+RLQAAAID8sOAXz7+kHgC5oWUctIyDlnHQMgY6ZlDTn4MPAAAAYGC4gw8AAAAEwoIPAAAABMKCX6PM7CIz+x8zW2xmX+3h9Veb2W/MbH7l23Up5kTfzOwBM1tnZr/u5fVmZndVOr9qZqcM9oyoThUtzzGzTZ3O5M2DPSP6Z2aHm9mLZvaamS0ws7/s4RrOZQFU2ZJzWQBmtq+Z/dLMWiotb+nhmmFm9ljlXL5iZkcO/qTFUdNPk7m3MrMhkr4n6QJJKyX9ysyecvfu/0bxY+7+xUEfEAPxoKR/kvRwL6+/WNIxlW+nS7qn8l/UngfVd0tJmuPuHxuccZBRSdKX3L3ZzEZIajKzF7r9/sq5LIZqWkqcyyLokHSuu281s6GS/tPMnnP3eZ2uuVbSBnc/2symSLpd0hUphi0C7uDXptMkLXb3N9z9bUn/Lmly4pmQgbvPlrS+j0smS3rYd5snaaSZHTo402EgqmiJAnD3Ne7eXPn+FkmvSTqs22WcywKosiUKoHLWtlZeHFr51v1ZYCZLeqjy/cclnWdmNkgjFg4Lfm06TNKbnV5eqZ5/0/pE5a+PHzezwwdnNOSs2tYohjMrf8X8nJkdn3oY9K3yV/wflPRKt1dxLgumj5YS57IQzGyImc2XtE7SC+7e67l095KkTZLeN7hTFgcLfm3q6SPS7h/JPi3pSHc/UdIs/f9HtSiWalqjGJolHeHuJ0n6R0k/TjwP+mBm+0t6QtJfufvm7q/u4adwLmtUPy05lwXh7rvc/WRJYyWdZmYndLuEczkALPi1aaWkznfkx0pa3fkCd3/L3TsqL94n6dRBmg356rc1isHdN7/zV8zu/hNJQ81sdOKx0IPK5/g+IekRd5/RwyWcy4LoryXnsnjcfaOkX0i6qNurfncuzaxe0gHi0yZ7xYJfm34l6RgzG29m+0iaIumpzhd0+3zQS7X7cw9RPE9J+kzlWTvOkLTJ3dekHgoDZ2bvf+fzQc3sNO3+/fWttFOhu0qj+yW95u7f6eUyzmUBVNOSc1kMZnaQmY2sfH8/SedLaut22VOSplW+f7mknzv/WmuveBadGuTuJTP7oqTnJQ2R9IC7LzCzWyU1uvtTkv7CzC7V7mcRWC/p6mQDo1dm9qikcySNNrOVkv5Ou794SO5+r6SfSPoTSYslbZN0TZpJ0Z8qWl4u6QYzK0naLmkKf/jUpA9LukpSa+XzfSXpbySNkziXBVNNS85lMRwq6aHKswjWSfqhuz/Tbe+5X9IPzGyxdu89U9KNW/uM/88BAACAOPgUHQAAACAQFnwAAAAgEBZ8AAAAIBAWfAAAACAQFnwAAAAgEBZ8AAAAIBAWfADAgJnZP5vZh1PPAQD4fTwPPgBgwCr/sNCp7r4r9SwAgK64gw8A6JGZnWpmL3Z6+QQz+y8z+4Ck1919V2/XJBkYACCJO/gAgF6Y2XBJi9z9sMrLMyTdLelESRvd/YHernH3WanmBoC9HXfwAQA9cvdtknaY2UgzO0XSgZXF/UJJ/9HPNQCAROpTDwAAqGkLJf2hpL+VdFPljv1Id1/d2zWSZGb1kv5ekkta7u53DerUALAXY8EHAPRlgaRrtPtTOl82s0mSXuzrmsqP3SBppru/NHijAgAkFnwAQN8WSHpI0h9VXr5Y0uP9XCNJp0i6Z49PBwD4PXyRLQCgambWLOl0d9/Zz3WTJV0qab2kb7n7+sGYDwDAgg8AAACEwrPoAAAAAIGw4AMAAACBsOADAAAAgbDgAwAAAIGw4AMAAACBsOADAAAAgbDgAwAAAIGw4AMAAACBsOADAAAAgfwfrbxx1nv2OvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x1112.46 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This cell is for plotting the Maxwell loops along one specified isotherm (chosen with myIdx)\n",
    "T_iso = T_sat[myIdx]\n",
    "tau_iso = T_crt/T_iso\n",
    "p_iso = p_vap[myIdx]\n",
    "print(\"tau_iso = \" + str(tau_iso))\n",
    "print(\"T_iso = \" + str(T_iso))\n",
    "print(\"p_sat(T_iso) = \" + str(p_iso))\n",
    "print(\"d_vap(T_iso) = \" + str(d_vap[myIdx]))\n",
    "print(\"d_liq(T_iso) = \" + str(d_liq[myIdx]))\n",
    "\n",
    "# copy coeffs of that single point\n",
    "c = x[myIdx,:]\n",
    "print(\"coeffs = \" + str(c))\n",
    "      \n",
    "# get a density range\n",
    "d_min = 0.8*d_vap[myIdx] \n",
    "#d_min = d_trp_vap\n",
    "# d_max = 1.9*d_liq[myIdx]\n",
    "d_max = PropsSI('D','T',T_iso,'P',p_max,FluidName)\n",
    "d_max = d_trp_liq\n",
    "rhos = np.linspace(d_min, d_max, num=nPoints)\n",
    "deltas = rhos/d_crt\n",
    "# for plotting, we will use volume (d_min is high v, d_max is low v)\n",
    "vs = 1/rhos\n",
    "\n",
    "# calculate Helmholtz energy and pressure for that density range, at T_iso\n",
    "# stable\n",
    "fs = np.ones(nPoints)\n",
    "ps = np.ones(nPoints)\n",
    "# equation of state, without chacking phase\n",
    "fes = np.ones(nPoints)\n",
    "pes = np.ones(nPoints)\n",
    "# meta-stable\n",
    "fms = np.ones(nPoints)\n",
    "pms = np.ones(nPoints)\n",
    "for idx in range(0,nPoints):\n",
    "    # stable\n",
    "    HEOS.unspecify_phase()\n",
    "    HEOS.update(CP.DmassT_INPUTS, rhos[idx], T_iso)  \n",
    "    #fs[idx] = Rs*T_iso*(HEOS.alpha0() + HEOS.alphar())\n",
    "    fs[idx] = HEOS.umass() - T_iso*HEOS.smass()\n",
    "    ps[idx] = HEOS.p()\n",
    "    # eos\n",
    "    HEOS.specify_phase(CP.iphase_liquid)\n",
    "    HEOS.update(CP.DmassT_INPUTS, rhos[idx], T_iso)\n",
    "    fes[idx] = HEOS.umass() - T_iso*HEOS.smass()\n",
    "    pes[idx] = HEOS.p()\n",
    "    # meta stable interpolation\n",
    "    # p = -(df/dv)_T\n",
    "    # s = -(df/dT)_v\n",
    "    # cv = T*(ds/dT)_v = -T*(dsf/dT2)_v\n",
    "    fms[idx]  = Rs*T_crt*( +c[0]/tau_iso -c[1]/tau_iso/deltas[idx] +c[2]*log(deltas[idx]) +c[3]*deltas[idx]    +c[4]*deltas[idx]**2/2 +c[5]*deltas[idx]**3/3 +c[6]*deltas[idx]**4/4 +c[7]*deltas[idx]**5/5 )\n",
    "    pms[idx]  = Rs*T_crt*d_crt*( 0       +c[1]/tau_iso             +c[2]*deltas[idx]      +c[3]*deltas[idx]**2 +c[4]*deltas[idx]**3   +c[5]*deltas[idx]**4   +c[6]*deltas[idx]**5   +c[7]*deltas[idx]**6   )\n",
    "\n",
    "# now plot \n",
    "plt.figure(figsize=(width,width*2/1/golden))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(vs/v_crt, fs/Rs/T_iso, color='red', label='stable equilibrium')\n",
    "plt.plot(vs/v_crt, fes/Rs/T_iso, color='green', label='EoS')\n",
    "plt.plot(vs/v_crt, fms/Rs/T_iso, color='blue', label='metastable Maxwell loop')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel(r'$\\alpha$')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(vs/v_crt, ps/p_crt, color='red', label='stable equilibrium')\n",
    "plt.plot(vs/v_crt, pes/p_crt, color='green', label='EoS')\n",
    "plt.plot(vs/v_crt, pms/p_crt, color='blue', label='metastable Maxwell loop')\n",
    "plt.plot(d_crt/d_liq[myIdx], p_iso/p_crt, 'b|', markersize=20)\n",
    "plt.plot(d_crt/d_vap[myIdx], p_iso/p_crt, 'r|', markersize=20)\n",
    "plt.ylim(top=7)\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel(r'$p/p_c$')\n",
    "plt.xlabel(r'$v/v_c$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy coeffs for all points\n",
    "c0 = x[:,0]\n",
    "c1 = x[:,1]\n",
    "c2 = x[:,2]\n",
    "c3 = x[:,3]\n",
    "c4 = x[:,4]\n",
    "c5 = x[:,5]\n",
    "c6 = x[:,6]\n",
    "c7 = x[:,7]\n",
    "\n",
    "# plot the values of all 8 coeffs over T_sat\n",
    "plt.figure(figsize=(width,width*4/2/golden))\n",
    "\n",
    "plt.subplot(4,2,1)\n",
    "plt.plot(T_sat, c0, label='c0')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,2)\n",
    "plt.plot(T_sat, c1, label='c1')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,3)\n",
    "plt.plot(T_sat, c2, label='c2')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,4)\n",
    "plt.plot(T_sat, c3, label='c3')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,5)\n",
    "plt.plot(T_sat, c4, label='c4')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,6)\n",
    "plt.plot(T_sat, c5, label='c5')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,7)\n",
    "plt.plot(T_sat, c6, label='c6')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')\n",
    "\n",
    "plt.subplot(4,2,8)\n",
    "plt.plot(T_sat, c7, label='c7')\n",
    "plt.grid(b=True, linestyle=':')\n",
    "plt.minorticks_on()\n",
    "plt.legend(loc='upper center')"
   ]
  }
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